# Modeling Energy Demand—A Systematic Literature Review

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

## 3. Classification of Techniques

#### 3.1. Statistical Techniques

#### 3.2. Machine Learning Techniques

#### 3.3. Metaheuristic Techniques

#### 3.4. Stochastic, Fuzzy and Grey Systems Theory Techniques

#### 3.5. Engineering-Based Techniques

## 4. Results

#### 4.1. Sectors and Energy Carriers

#### 4.2. Techniques and Input Data

- “Contributions” refers to the number of relevant articles.
- “Impact” describes the importance (high, medium, low) of the data type for the respective technique considering different use-cases.
- “Drawbacks” refers to the weaknesses and limitations of the data-technique combination.

#### 4.3. Spatiotemporal Level of Detail

#### 4.4. Prediction Accuracy

#### 4.5. Measures for Improvement of Accuracy

#### 4.6. Summary of Results

## 5. Discussion

## 6. Challenges and Future Research Directions

## 7. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Table A1.**Characteristics of existing literature reviews. Overview of existing literature reviews by content. In each line, black squares (■) indicate topics covered in the given review. Only seven literature reviews used a systematic approach. Most reviews cover more than one sector or energy carrier and are concerned with analyzing model inputs (demand drivers). Few reviews show the number of articles reviewed. Only the present article covers all aspects.

Systematic | Energy Carriers | Sectors | Building Focused | Demand Drivers | Reviewed Articles | References | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Electricity | Thermal | Natural gas | Primary energy | Residential | Commercial | Industries | All sectors | |||||

■ | ■ | ■ | ■ | 41 | [9] | |||||||

■ | ■ | ■ | ■ | ■ | n/a | [11] | ||||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | 63 | [10] | |||

■ | ■ | ■ | ■ | ■ | ■ | 483 | [4] | |||||

■ | ■ | ■ | ■ | ■ | ■ | 130 | [12] | |||||

■ | ■ | ■ | ■ | ■ | 39 | [13] | ||||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | 116 | [14] | ||||

■ | ■ | ■ | ■ | ■ | n/a | [205] | ||||||

■ | ■ | ■ | ■ | ■ | n/a | [206] | ||||||

■ | ■ | ■ | n/a | [207] | ||||||||

■ | ■ | ■ | ■ | ■ | 31 | [208] | ||||||

■ | ■ | ■ | n/a | [5] | ||||||||

■ | ■ | n/a | [209] | |||||||||

■ | ■ | ■ | ■ | ■ | 50 | [23] | ||||||

■ | ■ | ■ | n/a | [210] | ||||||||

■ | ■ | ■ | ■ | n/a | [211] | |||||||

■ | ■ | ■ | n/a | [212] | ||||||||

■ | n/a | [213] | ||||||||||

■ | ■ | ■ | n/a | [214] | ||||||||

■ | ■ | ■ | ■ | n/a | [215] | |||||||

■ | ■ | ■ | ■ | ■ | ■ | n/a | [216] | |||||

■ | ■ | ■ | ■ | n/a | [217] | |||||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | n/a | [128] | |||

■ | ■ | ■ | ■ | ■ | n/a | [218] | ||||||

■ | ■ | ■ | ■ | ■ | n/a | [219] | ||||||

■ | ■ | ■ | ■ | ■ | ■ | n/a | [220] | |||||

■ | ■ | ■ | 17 | [221] | ||||||||

■ | ■ | n/a | [222] | |||||||||

■ | ■ | ■ | n/a | [223,224] | ||||||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | 419 | This review |

**Table A2.**Keyword compilation. The table contains the keywords used for the literature research arranged by thematic groups. Keywords in the bottom cell are explicitly excluded, which leads to a more precise search result. The * is a truncation operator to retrieve words with variant zero to many characters.

Energy | Demand | Modeling |
---|---|---|

Electric * | Demand | Forecast * |

Natural gas | Consumption | Estimat * |

Heat | Load | Predict * |

Requirement | Project * | |

Intensity | Simulation | |

Disaggregation | ||

Planning | ||

Model * | ||

Bottom-up | ||

Top-down | ||

Excluded keywords: storage, carbon, emission, price, optimization, vehicle, climate | ||

Search string applied to the Web of Science Core Collection on 1 May 2021: (TI = ((electric* OR “natural gas” OR heat) AND (demand OR consumption OR load OR requirement OR intensity) AND (forecast* OR estimat* OR predict* OR project* OR simulation OR disaggregation OR planning OR model* OR “bottom-up” OR “top-down”)) NOT TS = (storage OR carbon OR emission OR price OR optimization OR vehicle OR climate)) |

**Table A3.**Articles sorted by energy carrier and sector. A compilation of the results of the analysis regarding energy carriers and sectors, which allows for direct tracing of the respective references.

**Table A5.**Techniques and input data used per article (2/4). Continuation of Table A4.

Method | Input | References |
---|---|---|

Clustering | Historic energy demand | [21,48,49,81,82,91,92,93,109,114,118,155,163,232,234,235,236,263,268,279,322,323,326,328,342,353,355,360,369,372,395,403,428,476] |

Weather data | [21,82,91,92,93,114,118,163,263,275,348,353,476] | |

Calendar data | [48,92,93,109,114,118,235,236,263,326,342,348] | |

Demographic or economic data | [21,49,81,82,93,232,234,348,395] | |

Technical system data | [82,91,93,353,395] | |

Usage or behavioral data | [348,395] | |

Energy prices | [49] | |

Ensemble learning | Historic energy demand | [94,95,96,97,100,106,114,120,130,132,133,138,162,168,355,363,407,449,476] |

Weather data | [95,96,97,106,114,130,132,138,363,407,449,476] | |

Calendar data | [94,95,97,106,114,120,138,162,449] | |

Demographic or economic data | [133] | |

Technical system data | [96,144,449] | |

Usage or behavioral data | [96] | |

Energy prices | [96] | |

Deep learning | Historic energy demand | [21,72,73,106,153,164,165,166,167,229,248,267,278,293,325,352,366] |

Weather data | [21,106,153,164,165,229,267,325,432] | |

Calendar data | [73,106,164,165,229,293,325] | |

Demographic or economic data | [21] | |

Technical system data | [366] | |

Usage or behavioral data | [432] | |

Energy prices | n/a | |

Bayesian algorithms | Historic energy demand | [39,98,99,145,146,164,249,250,405] |

Weather data | [39,98,145,146,164,239,345,405] | |

Calendar data | [39,98,146,164,239] | |

Demographic or economic data | [99,345] | |

Technical system data | [269,345] | |

Usage or behavioral data | [98] | |

Energy prices | [98] | |

Decision trees | Historic energy demand | [100,101,228,407,446,449,468] |

Weather data | [101,407,446,449,468] | |

Calendar data | [101,228,446,449,468] | |

Demographic or economic data | [101,446,468] | |

Technical system data | [101,457,468] | |

Usage or behavioral data | n/a | |

Energy prices | n/a |

**Table A6.**Techniques and input data used per article (3/4). Continuation of Table A5.

**Table A7.**Techniques and input data used per article (4/4). Continuation of Table A4.

Method | Input | References |
---|---|---|

Meta- heuristic | Historic energy demand | [34,37,39,40,49,71,72,73,112,116,117,136,150,163,175,189,190,191,192,254,363,407,427,437,446,447] |

Weather data | [22,37,39,40,112,136,150,163,175,191,254,363,407,427,437,446,447] | |

Calendar data | [39,73,112,136,150,446,447] | |

Demographic or economic data | [34,40,49,189,190,446,447] | |

Technical system data | [22,37] | |

Usage or behavioral data | [40] | |

Energy prices | [49,190,427] | |

Engineering-based | Historic energy demand | [53,75,78,85,129,186,187,287,291,326,329,341,353,359,368,370,371,374,408,431,469] |

Weather data | [52,53,54,74,75,77,78,187,194,329,332,334,347,353,368,408,450,466,467,469,480,481] | |

Calendar data | [52,77,121,122,326,332,347,375] | |

Demographic or economic data | [53,75,85,122,226,233,291,295,330,334,347,374,408,431,441,481] | |

Technical system data | [19,52,54,74,75,77,85,121,186,187,188,194,274,295,329,330,332,334,341,347,353,374,375,406,409,415,417,429,450,466,469,471,474,480,481,482,483] | |

Usage or behavioral data | [19,52,54,121,122,330,334,370,375,450,481,483] | |

Energy prices | [295,332] |

**Table A8.**Techniques and level of detail per article (1/2). Compiled results of the analysis on techniques, temporal horizon, and spatial resolution. The table follows a matrix structure: all articles referenced within a cell rely on the category of techniques specified.

**Table A9.**Techniques and level of detail per article (2/2). Continuation of Table A6.

Method | Temporal Horizon | Spatial Resolution | References |
---|---|---|---|

Stochastic/ Fuzzy/ Grey | Short | Appliance | [45,163,164,418] |

Building/household | [20,40,46,52,59,66,98,112,335,392,393] | ||

Regional | [30,79,113,135,176,276,299,434] | ||

National | [16,118,243,257,294,317] | ||

Medium | Appliance | n/a | |

Building/household | [64,75,324,349,385,470] | ||

Regional | [33,68,69,272,411] | ||

National | [246,251] | ||

Long | Appliance | n/a | |

Building/household | [114,321,327,347,382,414,463] | ||

Regional | [34,47,48,70,270,321,334,336,362] | ||

National | [18,29,49,51,121,133,192,284,285,435] | ||

Meta- heuristic | Short | Appliance | [163] |

Building/household | [40,112,117,175,387,407,413,437] | ||

Regional | [37,71,116,136,320] | ||

National | [73] | ||

Medium | Appliance | n/a | |

Building/household | [446] | ||

Regional | [254] | ||

National | [191] | ||

Long | Appliance | n/a | |

Building/household | n/a | ||

Regional | [34,150] | ||

National | [49,189,190,192] | ||

Engineering-based | Short | Appliance | [274,406,415,467,482] |

Building/household | [52,186,332,461] | ||

Regional | n/a | ||

National | [330] | ||

Medium | Appliance | [474,480] | |

Building/household | [19,74,75,78,188,466] | ||

Regional | [129] | ||

National | [368] | ||

Long | Appliance | [194,329,375,483] | |

Building/household | [54,77,85,341,347,353,463,481] | ||

Regional | [187,226,233,287,291,295,334,374,441] | ||

National | [126] |

**Figure A1.**Boxplot of MAPE values by different temporal resolutions. The same mode of display as in Figure 10. This shows that temporal resolution (length of time steps) does not seem to necessarily have an impact on accuracy measured by MAPE.

**Figure A2.**Boxplot of MAPE values by different temporal horizons. The same mode of display as in Figure 10. This shows that the length of the temporal horizon does not seem to necessarily have an impact on accuracy measured by MAPE.

**Figure A3.**Technical systems analyzed. This shows that most articles focus on power grids and buildings.

## References

- Bai, L.; Li, F.; Cui, H.; Jiang, T.; Sun, H.; Zhu, J. Interval Optimization Based Operating Strategy for Gas-Electricity Integrated Energy Syst. Considering Demand Response and Wind Uncertainty. Appl. Energy
**2016**, 167, 270–279. [Google Scholar] [CrossRef][Green Version] - Lopion, P.; Markewitz, P.; Robinius, M.; Stolten, D. A Review of Current Challenges and Trends in Energy Syst. Modeling. Renew. Sustain. Energy Rev.
**2018**, 96, 156–166. [Google Scholar] [CrossRef] - Birol, F. The Investment Implications of Global Energy Trends. Oxf. Rev. Econ. Policy
**2005**, 21, 145–153. [Google Scholar] [CrossRef] - Debnath, K.B.; Mourshed, M. Forecasting Methods in Energy Planning Models. Renew. Sustain. Energy Rev.
**2018**, 88, 297–325. [Google Scholar] [CrossRef][Green Version] - Hong, T.; Fan, S. Probabilistic Electric Load Forecasting: A Tutorial Review. Int. J. Forecast.
**2016**, 32, 914–938. [Google Scholar] [CrossRef] - Bhattacharyya, S.C.; Timilsina, G.R. Energy Demand Models For Policy Formulation: A Comparative Study Of Energy Demand Models; Policy Research Working Papers; The World Bank: Washington, DC, USA, 2009. [Google Scholar]
- Durach, C.F. A Theoretical and Practical Contribution to Supply Chain Robustness: Developing a Schema for Robustness in Dyads; Straube, F., Baumgartner, H., Klinker, R., Eds.; Schriftenreihe Logistik der Technischen Universität Berlin; Universitätsverlag TU Berlin: Berlin, Germany, 2016; Volume 33, ISBN 978-3-7983-2813-6. [Google Scholar]
- Tranfield, D.; Denyer, D.; Smart, P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag.
**2003**, 14, 207–222. [Google Scholar] [CrossRef] - Kuster, C.; Rezgui, Y.; Mourshed, M. Electrical Load Forecasting Models: A Critical Systematic Review. Sustain. Cities Soc.
**2017**, 35, 257–270. [Google Scholar] [CrossRef] - Amasyali, K.; El-Gohary, N.M. A Review of Data-Driven Building Energy Consumption Prediction Studies. Renew. Sustain. Energy Rev.
**2018**, 81, 1192–1205. [Google Scholar] [CrossRef] - Frederiks, E.; Stenner, K.; Hobman, E.; Frederiks, E.R.; Stenner, K.; Hobman, E.V. The Socio-Demographic and Psychological Predictors of Residential Energy Consumption: A Comprehensive Review. Energies
**2015**, 8, 573–609. [Google Scholar] [CrossRef][Green Version] - Riva, F.; Tognollo, A.; Gardumi, F.; Colombo, E. Long-Term Energy Planning and Demand Forecast in Remote Areas of Developing Countries: Classification of Case Studies and Insights from a Modelling Perspective. Energy Strategy Rev.
**2018**, 20, 71–89. [Google Scholar] [CrossRef][Green Version] - Šebalj, D.; Mesarić, J.; Dujak, D. Analysis of Methods and Techniques for Prediction of Natural Gas Consumption: A Literature Review. J. Inf. Organ. Sci.
**2019**, 43, 99–117. [Google Scholar] [CrossRef] - Wei, N.; Li, C.; Peng, X.; Zeng, F.; Lu, X. Conventional Models and Artificial Intelligence-Based Models for Energy Consumption Forecasting: A Review. J. Pet. Sci. Eng.
**2019**, 181, 106187. [Google Scholar] [CrossRef] - Matthews, T. LibGuides: Web of Science Platform: Web of Science: Summary of Coverage. Available online: https://clarivate.libguides.com/webofscienceplatform/coverage (accessed on 3 September 2019).
- Shieh, H.-L.; Chen, F.-H. Forecasting for Ultra-Short-Term Electric Power Load Based on Integrated Artificial Neural Networks. Symmetry
**2019**, 11, 1063. [Google Scholar] [CrossRef][Green Version] - Syah, I.F.; Abdullah, M.P.; Syadli, H.; Hassan, M.Y.; Hussin, F. Data Selection Test Method for Better Prediction Of Building Electricity Consumption. J. Teknol.
**2016**, 78, 67–72. [Google Scholar] [CrossRef] - Ma, X.; Liu, Z. Application of a Novel Time-Delayed Polynomial Grey Model to Predict the Natural Gas Consumption in China. J. Comput. Appl. Math.
**2017**, 324, 17–24. [Google Scholar] [CrossRef] - Chuan, L.; Ukil, A. Modeling and Validation of Electrical Load Profiling in Residential Buildings in Singapore. IEEE Trans. Power Syst.
**2015**, 30, 2800–2809. [Google Scholar] [CrossRef][Green Version] - Sancho-Tomás, A.; Sumner, M.; Robinson, D. A Generalised Model of Electrical Energy Demand from Small Household Appliances. Energy Build.
**2017**, 135, 350–366. [Google Scholar] [CrossRef] - Ye, C.; Ding, Y.; Wang, P.; Lin, Z. A Data-Driven Bottom-Up Approach for Spatial and Temporal Electric Load Forecasting. IEEE Trans. Power Syst.
**2019**, 34, 1966–1979. [Google Scholar] [CrossRef] - Tien Bui, D.; Moayedi, H.; Anastasios, D.; Kok Foong, L. Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models. Appl. Sci.
**2019**, 9, 3543. [Google Scholar] [CrossRef][Green Version] - Fallah, S.; Deo, R.; Shojafar, M.; Conti, M.; Shamshirband, S. Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions. Energies
**2018**, 11, 596. [Google Scholar] [CrossRef][Green Version] - Motulsky, H.J.; Ransnas, L.A. Fitting Curves to Data Using Nonlinear Regression: A Practical and Nonmathematical Review. FASEB J.
**1987**, 1, 365–374. [Google Scholar] [CrossRef] - Huang, Y.; Yuan, Y.; Chen, H.; Wang, J.; Guo, Y.; Ahmad, T. A Novel Energy Demand Prediction Strategy for Residential Buildings Based on Ensemble Learning. Energy Procedia
**2019**, 158, 3411–3416. [Google Scholar] [CrossRef] - Charytoniuk, W.; Chen, M.S.; Van Olinda, P. Nonparametric Regression Based Short-Term Load Forecasting. IEEE Trans. Power Syst.
**1998**, 13, 725–730. [Google Scholar] [CrossRef] - Mangalova, E.; Shesterneva, O. Sequence of Nonparametric Models for GEFCom2014 Probabilistic Electric Load Forecasting. Int. J. Forecast.
**2016**, 32, 1023–1028. [Google Scholar] [CrossRef] - Ghalehkhondabi, I.; Ardjmand, E.; Weckman, G.R.; Young, W.A. An Overview of Energy Demand Forecasting Methods Published in 2005–2015. Energy Syst.
**2017**, 8, 411–447. [Google Scholar] [CrossRef] - Kavaklioglu, K. Principal Components Based Robust Vector Autoregression Prediction of Turkey’s Electricity Consumption. Energy Syst.
**2019**, 10, 889–910. [Google Scholar] [CrossRef] - Nagbe, K.; Cugliari, J.; Jacques, J. Short-Term Electricity Demand Forecasting Using a Functional State Space Model. Energies
**2018**, 11, 1120. [Google Scholar] [CrossRef][Green Version] - Russell, S.J.; Norvig, P. Artificial Intelligence: A Modern Approach; Prentice Hall series in artificial intelligence; Prentice Hall: Englewood Cliffs, NJ, USA, 1995; ISBN 978-0-13-103805-9. [Google Scholar]
- Singh, A.; Thakur, N.; Sharma, A. A Review of Supervised Machine Learning Algorithms. In Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development, New Delhi, India, 16–18 March 2016; pp. 1310–1315. [Google Scholar]
- Alamaniotis, M.; Bargiotas, D.; Tsoukalas, L.H. Towards Smart Energy Syst.: Application of Kernel Machine Regression for Medium Term Electricity Load Forecasting. SpringerPlus
**2016**, 5, 58. [Google Scholar] [CrossRef][Green Version] - Song, Z.; Niu, D.; Dai, S.; Xiao, X.; Wang, Y. Incorporating the Influence of China’s Industrial Capacity Elimination Policies in Electricity Demand Forecasting. Util. Policy
**2017**, 47, 1–11. [Google Scholar] [CrossRef] - Brownlee, J. Master Machine Learning Algorithms—Discover How They Work and Implement Them From Scratch; Machine Learning Mastery: Vermont, VIC, Australia, 2016. [Google Scholar]
- Tzanetos, A.; Dounias, G. An Application-Based Taxonomy of Nature Inspired Intelligent Algorithms; MDE Lab: Chios, Greece, 2019. [Google Scholar]
- Al-Shammari, E.T.; Keivani, A.; Shamshirband, S.; Mostafaeipour, A.; Yee, P.L.; Petković, D.; Ch, S. Prediction of Heat Load in District Heating Systems by Support Vector Machine with Firefly Searching Algorithm. Energy
**2016**, 95, 266–273. [Google Scholar] [CrossRef] - Nur, A.S.; Mohd Radzi, N.H.; Ibrahim, A.O. Artificial Neural Network Weight Optimization: A Review. Telkomnika Indones. J. Electr. Eng.
**2014**, 12, 6897–6902. [Google Scholar] [CrossRef] - Saleh, A.I.; Rabie, A.H.; Abo-Al-Ez, K.M. A Data Mining Based Load Forecasting Strategy for Smart Electrical Grids. Adv. Eng. Inform.
**2016**, 30, 422–448. [Google Scholar] [CrossRef] - Eseye, A.T.; Lehtonen, M.; Tukia, T.; Uimonen, S.; John Millar, R. Machine Learning Based Integrated Feature Selection Approach for Improved Electricity Demand Forecasting in Decentralized Energy Syst. IEEE Access
**2019**, 7, 91463–91475. [Google Scholar] [CrossRef] - Zimmermann, H.-J. Fuzzy Set Theory—And Its Applications; Springer Netherlands: Dordrecht, 2001; ISBN 978-94-010-3870-6. [Google Scholar]
- Hájek, P.; Godo, L.; Esteva, F. Fuzzy Logic and Probability. arXiv
**2013**, arXiv:1302.4953. [Google Scholar] - Riedewald, F. Comparison of Deterministic, Stochastic and Fuzzy Logic Uncertainty Modelling for Capacity Extension Projects of DI/WFI Pharmaceutical Plant Utilities with Variable/Dynamic Demand. Ph.D. Thesis, University College Cork, Cork, Ireland, 2011. [Google Scholar]
- Ferrández-Pastor, F.J.; Mora-Mora, H.; Sánchez-Romero, J.L.; Nieto-Hidalgo, M.; García-Chamizo, J.M. Interpreting Human Activity from Electrical Consumption Data Using Reconfigurable Hardware and Hidden Markov Models. J. Ambient Intell. Human. Comput.
**2017**, 8, 469–483. [Google Scholar] [CrossRef] - Andersson, M. Modeling Electricity Load Curves with Hidden Markov Models for Demand-Side Management Status Estimation: Modeling Electricity Load Curves. Int. Trans. Electr. Energy Syst.
**2017**, 27, e2265. [Google Scholar] [CrossRef] - Duan, Q.; Liu, J.; Zhao, D. A Fast Algorithm for Short Term Electric Load Forecasting by a Hidden Semi-Markov Process. J. Stat. Comput. Simul.
**2019**, 89, 831–843. [Google Scholar] [CrossRef] - Ismail, Z.; Efendi, R.; Deris, M.M. Application of Fuzzy Time Series Approach in Electric Load Forecasting. New Math. Nat. Comput.
**2015**, 11, 229–248. [Google Scholar] [CrossRef] - Laouafi, A.; Mordjaoui, M.; Laouafi, F.; Boukelia, T.E. Daily Peak Electricity Demand Forecasting Based on an Adaptive Hybrid Two-Stage Methodology. Int. J. Electr. Power Energy Syst.
**2016**, 77, 136–144. [Google Scholar] [CrossRef] - Mollaiy-Berneti, S. Optimal Design of Adaptive Neuro-Fuzzy Inference System Using Genetic Algorithm for Electricity Demand Forecasting in Iranian Industry. Soft Comput.
**2016**, 20, 4897–4906. [Google Scholar] [CrossRef] - Tien, T.-L. A New Grey Prediction Model FGM(1, 1). Soft Comput.
**2009**, 49, 1416–1426. [Google Scholar] [CrossRef] - Hu, Y.-C. Electricity Consumption Prediction Using a Neural-Network-Based Grey Forecasting Approach. J. Oper. Res. Soc.
**2017**, 68, 1259–1264. [Google Scholar] [CrossRef] - McKenna, E.; Thomson, M. High-Resolution Stochastic Integrated Thermal–Electrical Domestic Demand Model. Appl. Energy
**2016**, 165, 445–461. [Google Scholar] [CrossRef][Green Version] - Rehfeldt, M.; Fleiter, T.; Toro, F. A Bottom-up Estimation of the Heating and Cooling Demand in European Industry. Energy Effic.
**2018**, 11, 1057–1082. [Google Scholar] [CrossRef] - Nouvel, R.; Zirak, M.; Coors, V.; Eicker, U. The Influence of Data Quality on Urban Heating Demand Modeling Using 3D City Models. Comput. Environ. Urban Syst.
**2017**, 64, 68–80. [Google Scholar] [CrossRef] - Li, C. GIS for Urban Energy Analysis. In Comprehensive Geographic Information Systems; Elsevier: Amsterdam, The Netherlands, 2018; pp. 187–195. ISBN 978-0-12-804793-4. [Google Scholar]
- Prerna, R.; Gangopadhyay, P.K. Demand Forecasting of Electricity and Optimal Locationing of Transformer Locations Using Geo-Spatial Techniques: A Case Study of Districts of Bihar, India. Appl. Spat. Anal. Policy
**2015**, 8, 69–83. [Google Scholar] [CrossRef] - D’Alonzo, V.; Novelli, A.; Vaccaro, R.; Vettorato, D.; Albatici, R.; Diamantini, C.; Zambelli, P. A Bottom-up Spatially Explicit Methodology to Estimate the Space Heating Demand of the Building Stock at Regional Scale. Energy Build.
**2020**, 206, 109581. [Google Scholar] [CrossRef] - Ivanov, V.V.; Kryanev, A.V.; Osetrov, E.S. Forecasting the Daily Electricity Consumption in the Moscow Region Using Artificial Neural Networks. Phys. Part. Nuclei Lett.
**2017**, 14, 647–657. [Google Scholar] [CrossRef] - Laouafi, A.; Mordjaoui, M.; Haddad, S.; Boukelia, T.E.; Ganouche, A. Online Electricity Demand Forecasting Based on an Effective Forecast Combination Methodology. Electr. Power Syst. Res.
**2017**, 148, 35–47. [Google Scholar] [CrossRef] - Ren, Y.; Suganthan, P.N.; Srikanth, N.; Amaratunga, G. Random Vector Functional Link Network for Short-Term Electricity Load Demand Forecasting. Inf. Sci.
**2016**, 367–368, 1078–1093. [Google Scholar] [CrossRef] - Kipping, A.; Trømborg, E. Modeling Hourly Consumption of Electricity and District Heat in Non-Residential Buildings. Energy
**2017**, 123, 473–486. [Google Scholar] [CrossRef] - Morita, K.; Shiromaru, H.; Manabe, Y.; Kato, T.; Funabashi, T.; Suzuoki, Y. A Study on Estimation of Aggregated Electricity Demand for One-Hour-Ahead Forecast. Appl. Therm. Eng.
**2017**, 114, 1443–1448. [Google Scholar] [CrossRef] - Sandels, C.; Widén, J.; Nordström, L.; Andersson, E. Day-Ahead Predictions of Electricity Consumption in a Swedish Office Building from Weather, Occupancy, and Temporal Data. Energy Build.
**2015**, 108, 279–290. [Google Scholar] [CrossRef] - Shepero, M.; van der Meer, D.; Munkhammar, J.; Widén, J. Residential Probabilistic Load Forecasting: A Method Using Gaussian Process Designed for Electric Load Data. Appl. Energy
**2018**, 218, 159–172. [Google Scholar] [CrossRef] - Ziegler, F.; Seim, S.; Verwiebe, P.; Müller-Kirchenbauer, J. A Probabilistic Modelling Approach for Residential Load Profiles. Energ. Ressour.
**2020**, 1–28. [Google Scholar] [CrossRef] - Ge, Y.; Zhou, C.; Hepburn, D.M. Domestic Electricity Load Modelling by Multiple Gaussian Functions. Energy Build.
**2016**, 126, 455–462. [Google Scholar] [CrossRef][Green Version] - Verdejo, H.; Awerkin, A.; Becker, C.; Olguin, G. Statistic Linear Parametric Techniques for Residential Electric Energy Demand Forecasting. A Review and an Implementation to Chile. Renew. Sustain. Energy Rev.
**2017**, 74, 512–521. [Google Scholar] [CrossRef] - Dong, B.; Li, Z.; Rahman, S.M.M.; Vega, R. A Hybrid Model Approach for Forecasting Future Residential Electricity Consumption. Energy Build.
**2016**, 117, 341–351. [Google Scholar] [CrossRef] - Lou, C.W.; Dong, M.C. A Novel Random Fuzzy Neural Networks for Tackling Uncertainties of Electric Load Forecasting. Int. J. Electr. Power Energy Syst.
**2015**, 73, 34–44. [Google Scholar] [CrossRef] - Ali, D.; Yohanna, M.; Ijasini, P.M.; Garkida, M.B. Application of Fuzzy – Neuro to Model Weather Parameter Variability Impacts on Electrical Load Based on Long-Term Forecasting. Alex. Eng. J.
**2018**, 57, 223–233. [Google Scholar] [CrossRef] - Zhang, X.; Wang, J.; Zhang, K. Short-Term Electric Load Forecasting Based on Singular Spectrum Analysis and Support Vector Machine Optimized by Cuckoo Search Algorithm. Electr. Power Syst. Res.
**2017**, 146, 270–285. [Google Scholar] [CrossRef] - Khan, G.M.; Zafari, F. Dynamic Feedback Neuro-Evolutionary Networks for Forecasting the Highly Fluctuating Electrical Loads. Genet. Program. Evolvable Mach.
**2016**, 17, 391–408. [Google Scholar] [CrossRef] - Khan, G.M.; Arshad, R. Electricity Peak Load Forecasting Using CGP Based Neuro Evolutionary Techniques. Int. J. Comput. Intell. Syst.
**2016**, 9, 376–395. [Google Scholar] [CrossRef][Green Version] - Perera, D.; Skeie, N.-O. Comparison of Space Heating Energy Consumption of Residential Buildings Based on Traditional and Model-Based Techniques. Buildings
**2017**, 7, 27. [Google Scholar] [CrossRef][Green Version] - Muthalib, M.K.; Nwankpa, C.O. Physically-Based Building Load Model for Electric Grid Operation and Planning. IEEE Trans. Smart Grid
**2017**, 8, 169–177. [Google Scholar] [CrossRef] - Fleiter, T.; Worrell, E.; Eichhammer, W. Barriers to Energy Effic in Industrial Bottom-up Energy Demand Models—A Review. Renew. Sustain. Energy Rev.
**2011**, 15, 3099–3111. [Google Scholar] [CrossRef] - Cao, J.; Liu, J.; Man, X. A United WRF/TRNSYS Method for Estimating the Heating/Cooling Load for the Thousand-Meter Scale Megatall Buildings. Appl. Therm. Eng.
**2017**, 114, 196–210. [Google Scholar] [CrossRef] - Nam, T.H.H.; Kubota, T.; Trihamdani, A.R. Impact of Urban Heat Island under the Hanoi Master Plan 2030 on Cooling Loads in Residential Buildings. Int. J. Built Environ. Sustain.
**2015**, 2, 48–61. [Google Scholar] [CrossRef] - Lopez, J.C.; Rider, M.J.; Wu, Q. Parsimonious Short-Term Load Forecasting for Optimal Operation Planning of Electrical Distribution Systems. IEEE Trans. Power Syst.
**2019**, 34, 1427–1437. [Google Scholar] [CrossRef][Green Version] - Xue, G.; Song, J.; Kong, X.; Pan, Y.; Qi, C.; Li, H. Prediction of Natural Gas Consumption for City-Level DHS Based on Attention GRU: A Case Study for a Northern Chinese City. IEEE Access
**2019**, 7, 130685–130699. [Google Scholar] [CrossRef] - Salvó, G.; Piacquadio, M.N. Multifractal Analysis of Electricity Demand as a Tool for Spatial Forecasting. Energy Sustain. Dev.
**2017**, 38, 67–76. [Google Scholar] [CrossRef] - Zhao, T.; Zhang, Y.; Chen, H. Spatio-Temporal Load Forecasting Considering Aggregation Features of Electricity Cells and Uncertainties in Input Variables. J. Electr. Eng. Technol.
**2018**, 13, 38–50. [Google Scholar] - Brabec, M.; Konár, O.; Kasanický, I.; Malý, M.; Pelikán, E. Semiparametric Modeling of the Spatiotemporal Trends in Natural Gas Consumption: Methodology, Results, and Consequences. Appl. Stoch. Models Bus. Ind.
**2020**, 36, 184–194. [Google Scholar] [CrossRef][Green Version] - Falchetta, G.; Noussan, M. Interannual Variation in Night-Time Light Radiance Predicts Changes in National Electricity Consumption Conditional on Income-Level and Region. Energies
**2019**, 12, 456. [Google Scholar] [CrossRef][Green Version] - Bednar, D.J.; Reames, T.G.; Keoleian, G.A. The Intersection of Energy and Justice: Modeling the Spatial, Racial/Ethnic and Socioeconomic Patterns of Urban Residential Heating Consumption and Efficiency in Detroit, Michigan. Energy Build.
**2017**, 143, 25–34. [Google Scholar] [CrossRef] - Deng, C.; Lin, W.; Ye, X.; Li, Z.; Zhang, Z.; Xu, G. Social Media Data as a Proxy for Hourly Fine-Scale Electric Power Consumption Estimation. Environ. Plan. A Econ. Space
**2018**, 50, 1553–1557. [Google Scholar] [CrossRef][Green Version] - Wang, H.; Tu, F.; Tu, B.; Feng, G.; Yuan, G.; Ren, H.; Dong, J. Neural Network Based Central Heating System Load Prediction and Constrained Control. Math. Probl. Eng.
**2018**, 2018, 2908608. [Google Scholar] [CrossRef][Green Version] - Mordjaoui, M.; Haddad, S.; Medoued, A.; Laouafi, A. Electric Load Forecasting by Using Dynamic Neural Network. Int. J. Hydrog. Energy
**2017**, 42, 17655–17663. [Google Scholar] [CrossRef] - Duan, J.; Qiu, X.; Ma, W.; Tian, X.; Shang, D. Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion. Entropy
**2018**, 20, 112. [Google Scholar] [CrossRef] [PubMed][Green Version] - Fan, G.-F.; Peng, L.-L.; Hong, W.-C.; Sun, F. Electric Load Forecasting by the SVR Model with Differential Empirical Mode Decomposition and Auto Regression. Neurocomputing
**2016**, 173, 958–970. [Google Scholar] [CrossRef] - Wang, J.; Li, G.; Chen, H.; Liu, J.; Guo, Y.; Sun, S.; Hu, Y. Energy Consumption Prediction for Water-Source Heat Pump System Using Pattern Recognition-Based Algorithms. Appl. Therm. Eng.
**2018**, 136, 755–766. [Google Scholar] [CrossRef] - Li, Y.; Guo, P.; Li, X. Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior. Algorithms
**2016**, 9, 80. [Google Scholar] [CrossRef][Green Version] - Melzi, F.N.; Same, A.; Zayani, M.H.; Oukhellou, L. A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors. Energies
**2017**, 10, 1446. [Google Scholar] [CrossRef][Green Version] - Huang, N.; Lu, G.; Xu, D. A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest. Energies
**2016**, 9, 767. [Google Scholar] [CrossRef][Green Version] - De Oliveira, E.M.; Cyrino Oliveira, F.L. Forecasting Mid-Long Term Electric Energy Consumption through Bagging ARIMA and Exponential Smoothing Methods. Energy
**2018**, 144, 776–788. [Google Scholar] [CrossRef] - Chen, K.; Jiang, J.; Zheng, F.; Chen, K. A Novel Data-Driven Approach for Residential Electricity Consumption Prediction Based on Ensemble Learning. Energy
**2018**, 150, 49–60. [Google Scholar] [CrossRef] - Khwaja, A.S.; Zhang, X.; Anpalagan, A.; Venkatesh, B. Boosted Neural Networks for Improved Short-Term Electric Load Forecasting. Electr. Power Syst. Res.
**2017**, 143, 431–437. [Google Scholar] [CrossRef] - Bassamzadeh, N.; Ghanem, R. Multiscale Stochastic Prediction of Electricity Demand in Smart Grids Using Bayesian Networks. Appl. Energy
**2017**, 193, 369–380. [Google Scholar] [CrossRef] - Zhang, W.; Yang, J. Forecasting Natural Gas Consumption in China by Bayesian Model Averaging. Energy Rep.
**2015**, 1, 216–220. [Google Scholar] [CrossRef][Green Version] - Huang, N.; Hu, Z.; Cai, G.; Yang, D. Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria. Entropy
**2016**, 18, 330. [Google Scholar] [CrossRef][Green Version] - Idowu, S.; Saguna, S.; Åhlund, C.; Schelén, O. Applied Machine Learning: Forecasting Heat Load in District Heating System. Energy Build.
**2016**, 133, 478–488. [Google Scholar] [CrossRef] - Cao, L.; Li, Y.; Zhang, J.; Jiang, Y.; Han, Y.; Wei, J. Electrical Load Prediction of Healthcare Buildings through Single and Ensemble Learning. Energy Rep.
**2020**, 6, 2751–2767. [Google Scholar] [CrossRef] - Leme, J.V.; Casaca, W.; Colnago, M.; Dias, M.A. Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models. Energies
**2020**, 13, 1407. [Google Scholar] [CrossRef][Green Version] - Akpinar, M.; Yumusak, N. Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods. Energies
**2016**, 9, 727. [Google Scholar] [CrossRef] - Boroojeni, K.G.; Amini, M.H.; Bahrami, S.; Iyengar, S.S.; Sarwat, A.I.; Karabasoglu, O. A Novel Multi-Time-Scale Modeling for Electric Power Demand Forecasting: From Short-Term to Medium-Term Horizon. Electr. Power Syst. Res.
**2017**, 142, 58–73. [Google Scholar] [CrossRef] - Qiu, X.; Suganthan, P.N.; Amaratunga, G.A.J. Ensemble Incremental Learning Random Vector Functional Link Network for Short-Term Electric Load Forecasting. Knowl. Based Syst.
**2018**, 145, 182–196. [Google Scholar] [CrossRef] - Ding, Y.; Zhang, Q.; Yuan, T.; Yang, K. Model Input Selection for Building Heating Load Prediction: A Case Study for an Office Building in Tianjin. Energy Build.
**2018**, 159, 254–270. [Google Scholar] [CrossRef] - Weide, L.; Demeng, K.; Jinran, W. A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting. Energies
**2017**, 10, 694. [Google Scholar] [CrossRef][Green Version] - Koo, B.-G.; Lee, H.-S.; Park, J. Short-Term Electric Load Forecasting Based on Wavelet Transform and GMDH. J. Electr. Eng. Technol.
**2015**, 10, 832–837. [Google Scholar] [CrossRef][Green Version] - Rana, M.; Koprinska, I. Forecasting Electricity Load with Advanced Wavelet Neural Networks. Neurocomputing
**2016**, 182, 118–132. [Google Scholar] [CrossRef] - Yaslan, Y.; Bican, B. Empirical Mode Decomposition Based Denoising Method with Support Vector Regression for Time Series Prediction: A Case Study for Electricity Load Forecasting. Measurement
**2017**, 103, 52–61. [Google Scholar] [CrossRef] - Panapakidis, I.P.; Dagoumas, A.S. Day-Ahead Natural Gas Demand Forecasting Based on the Combination of Wavelet Transform and ANFIS/Genetic Algorithm/Neural Network Model. Energy
**2017**, 118, 231–245. [Google Scholar] [CrossRef] - Chahkoutahi, F.; Khashei, M. A Seasonal Direct Optimal Hybrid Model of Computational Intelligence and Soft Comput. Techniques for Electricity Load Forecasting. Energy
**2017**, 140, 988–1004. [Google Scholar] [CrossRef] - Jovanovic, R.; Sretenovic, A. Various Multistage Ensembles for Prediction of Heating Energy Consumption. Modeling Identif. Control. A Nor. Res. Bull.
**2015**, 36, 119–132. [Google Scholar] [CrossRef][Green Version] - Jovanovic, R.; Sretenovic, A.; Zivkovic, B. Multistage Ensemble of Feedforward Neural Networks for Prediction of Heating Energy Consumption. Therm. Sci.
**2016**, 20, 1321–1331. [Google Scholar] [CrossRef][Green Version] - Akpinar, M.; Adak, M.; Yumusak, N. Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey. Energies
**2017**, 10, 781. [Google Scholar] [CrossRef][Green Version] - Bianchi, F.M.; Santis, E.D.; Rizzi, A.; Sadeghian, A. Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition. IEEE Access
**2015**, 3, 1931–1943. [Google Scholar] [CrossRef] - Brodowski, S.; Bielecki, A.; Filocha, M. A Hybrid System for Forecasting 24-h Power Load Profile for Polish Electric Grid. Appl. Soft Comput.
**2017**, 58, 527–539. [Google Scholar] [CrossRef] - Pang, Y.; Yao, B.; Zhou, X.; Zhang, Y.; Xu, Y.; Tan, Z. Hierarchical Electricity Time Series Forecasting for Integrating Consumption Patterns Analysis and Aggregation Consistency. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 13–19 July 2018; International Joint Conferences on Artificial Intelligence Organization: Stockholm, Sweden; pp. 3506–3512.
- Li, C.; Zheng, X.; Yang, Z.; Kuang, L. Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment. Wirel. Commun. Mob. Comput.
**2018**, 2018, 5018053. [Google Scholar] [CrossRef] - Fischer, D.; Härtl, A.; Wille-Haussmann, B. Model for Electric Load Profiles with High Time Resolution for German Households. Energy Build.
**2015**, 92, 170–179. [Google Scholar] [CrossRef] - Marszal-Pomianowska, A.; Heiselberg, P.; Kalyanova Larsen, O. Household Electricity Demand Profiles – A High-Resolution Load Model to Facilitate Modelling of Energy Flexible Buildings. Energy
**2016**, 103, 487–501. [Google Scholar] [CrossRef] - Xu, M.; Singh, S. China Liberalises Coal-Fired Power Pricing to Tackle Energy Crisis; Reuters: London, UK, 2021; Volume 1, p. 1. [Google Scholar]
- Labandeira, X.; Labeaga, J.M.; López-Otero, X. A Meta-Analysis on the Price Elasticity of Energy Demand. Energy Policy
**2017**, 102, 549–568. [Google Scholar] [CrossRef][Green Version] - Seim, S.; Verwiebe, P.; Blech, K.; Gerwin, C.; Müller-Kirchenbauer, J. Die Datenlandschaft der deutschen Energiewirtschaft; Zenodo: Meyrin, Switzerland, 2019. [Google Scholar] [CrossRef]
- Maçaira, P.; Elsland, R.; Oliveira, F.C.; Souza, R.; Fernandes, G. Forecasting Residential Electricity Consumption: A Bottom-up Approach for Brazil by Region. Energy Effic.
**2020**, 13, 911–934. [Google Scholar] [CrossRef] - Lewis, C.D. Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting; Butterworth S Scientific: London, UK; Boston, MA, USA, 1982; ISBN 978-0-408-00559-3. [Google Scholar]
- Wei, Y.; Zhang, X.; Shi, Y.; Xia, L.; Pan, S.; Wu, J.; Han, M.; Zhao, X. A Review of Data-Driven Approaches for Prediction and Classification of Building Energy Consumption. Renew. Sustain. Energy Rev.
**2018**, 82, 1027–1047. [Google Scholar] [CrossRef] - Hu, Z.; Ma, J.; Yang, L.; Li, X.; Pang, M. Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand. Sustainability
**2019**, 11, 1272. [Google Scholar] [CrossRef][Green Version] - Lee, J.; Kim, J.; Ko, W. Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method. Appl. Sci.
**2019**, 9, 1231. [Google Scholar] [CrossRef][Green Version] - Grmanová, G.; Laurinec, P.; Rozinajová, V.; Ezzeddine, A.B.; Lucká, M.; Lacko, P.; Vrablecová, P.; Návrat, P. Incremental Ensemble Learning for Electricity Load Forecasting. Acta Polytech. Hung.
**2016**, 13, 97–117. [Google Scholar] [CrossRef] - Shan, S.; Cao, B.; Wu, Z. Forecasting the Short-Term Electricity Consumption of Building Using a Novel Ensemble Model. IEEE Access
**2019**, 7, 88093–88106. [Google Scholar] [CrossRef] - Zhang, X.; Zhou, W. Forecast of China’s Natural Gas Consumption Using Mathematical Models. Energy Sources Part B Econ. Plan. Policy
**2018**, 13, 246–250. [Google Scholar] [CrossRef] - Liang, J.; Liang, Y. Analysis and Modeling for China’s Electricity Demand Forecasting Based on a New Mathematical Hybrid Method. Information
**2017**, 8, 33. [Google Scholar] [CrossRef][Green Version] - Lu, X.; Wang, J.; Cai, Y.; Zhao, J. Distributed HS-ARTMAP and Its Forecasting Model for Electricity Load. Appl. Soft Comput.
**2015**, 32, 13–22. [Google Scholar] [CrossRef] - Buitrago, J.; Asfour, S. Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs. Energies
**2017**, 10, 40. [Google Scholar] [CrossRef][Green Version] - Zhu, R.; Guo, W.; Gong, X. Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning. Energies
**2019**, 12, 3308. [Google Scholar] [CrossRef][Green Version] - Sigauke, C. Forecasting Medium-Term Electricity Demand in a South African Electric Power Supply System. J. Energy South. Afr.
**2017**, 28, 54–67. [Google Scholar] [CrossRef] - Ziel, F.; Liu, B. Lasso Estimation for GEFCom 2014 Probabilistic Electric Load Forecasting. Int. J. Forecast.
**2016**, 32, 1029–1037. [Google Scholar] [CrossRef][Green Version] - Takeda, H.; Tamura, Y.; Sato, S. Using the Ensemble Kalman Filter for Electricity Load Forecasting and Analysis. Energy
**2016**, 104, 184–198. [Google Scholar] [CrossRef] - He, Y.; Qin, Y.; Wang, S.; Wang, X.; Wang, C. Electricity Consumption Probability Density Forecasting Method Based on LASSO-Quantile Regression Neural Network. Appl. Energy
**2019**, 233–234, 565–575. [Google Scholar] [CrossRef][Green Version] - Lebotsa, M.E.; Sigauke, C.; Bere, A.; Fildes, R.; Boylan, J.E. Short Term Electricity Demand Forecasting Using Partially Linear Additive Quantile Regression with an Application to the Unit Commitment Problem. Appl. Energy
**2018**, 222, 104–118. [Google Scholar] [CrossRef][Green Version] - Yu, C.; Mirowski, P.; Ho, T.K. A Sparse Coding Approach to Household Electricity Demand Forecasting in Smart Grids. IEEE Trans. Smart Grid
**2017**, 8, 738–748. [Google Scholar] [CrossRef] - Srihari, J.; Santhi, B. Prediction of Heating and Cooling Load to Improve Energy Effic. of Buildings Using Machine Learning Techniques. J. Mech. Contin. Math. Sci.
**2018**, 13, 97–113. [Google Scholar] [CrossRef] - Kim, M.; Cha, J.; Lee, E.; Pham, V.; Lee, S.; Theera-Umpon, N. Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building. Energies
**2019**, 12, 1201. [Google Scholar] [CrossRef][Green Version] - Potočnik, P.; Strmčnik, E.; Govekar, E. Linear and Neural Network-Based Models for Short-Term Heat Load Forecasting. Stroj. Vestn. J. Mech. Eng.
**2015**, 61, 543–550. [Google Scholar] [CrossRef] - Cecati, C.; Kolbusz, J.; Rozycki, P.; Siano, P.; Wilamowski, B.M. A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies. IEEE Trans. Ind. Electron.
**2015**, 62, 6519–6529. [Google Scholar] [CrossRef] - Ismail, N.; Abdullah, S. Principal Component Regression with Artificial Neural Network to Improve Prediction of Electricity Demand. Int. Arab J. Inf. Technol.
**2016**, 13, 7. [Google Scholar] - Vu, D.H.; Muttaqi, K.M.; Agalgaonkar, A.P. A Variance Inflation Factor and Backward Elimination Based Robust Regression Model for Forecasting Monthly Electricity Demand Using Climatic Variables. Appl. Energy
**2015**, 140, 385–394. [Google Scholar] [CrossRef][Green Version] - Amber, K.P.; Aslam, M.W.; Hussain, S.K. Electricity Consumption Forecasting Models for Administration Buildings of the UK Higher Education Sector. Energy Build.
**2015**, 90, 127–136. [Google Scholar] [CrossRef] - Chen, H.-Y.; Lee, C.-H. Electricity Consumption Prediction for Buildings Using Multiple Adaptive Network-Based Fuzzy Inference System Models and Gray Relational Analysis. Energy Rep.
**2019**, 5, 1509–1524. [Google Scholar] [CrossRef] - Pepplow, L.A.; Betini, R.C.; Pereira, T.C.G. Forecasting the Electricity Consumption in a Higher Education Institution. Braz. Arch. Biol. Technol.
**2019**, 62, 1–10. [Google Scholar] [CrossRef] - Wei, N.; Li, C.; Duan, J.; Liu, J.; Zeng, F. Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model. Energies
**2019**, 12, 218. [Google Scholar] [CrossRef][Green Version] - Yuan, T.; Zhu, N.; Shi, Y.; Chang, C.; Yang, K.; Ding, Y. Sample Data Selection Method for Improving the Prediction Accuracy of the Heating Energy Consumption. Energy Build.
**2018**, 158, 234–243. [Google Scholar] [CrossRef] - Martínez-Álvarez, F.; Schmutz, A.; Asencio-Cortés, G.; Jacques, J. A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand. Energies
**2018**, 12, 94. [Google Scholar] [CrossRef][Green Version] - Gordillo-Orquera, R.; Lopez-Ramos, L.; Muñoz-Romero, S.; Iglesias-Casarrubios, P.; Arcos-Avilés, D.; Marques, A.; Rojo-Álvarez, J. Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings. Energies
**2018**, 11, 493. [Google Scholar] [CrossRef][Green Version] - Afshari, A.; Luiz A., F. Inverse Modeling of the Urban Energy System Using Hourly Electricity Demand and Weather Measurements, Part 1: Black-Box Model. Energy Build.
**2017**, 157, 126–138. [Google Scholar] [CrossRef] - Calili, R.F.; Souza, R.C.; Musafir, J.; Mendes Pinho, J.A. Correction of Load Curves Estimated by Electrical Appliances Ownership Surveys Using Mass Memory Meters. Energy Effic.
**2018**, 11, 261–272. [Google Scholar] [CrossRef] - Gunay, B.; Shen, W.; Newsham, G. Inverse Blackbox Modeling of the Heating and Cooling Load in Office Buildings. Energy Build.
**2017**, 142, 200–210. [Google Scholar] [CrossRef][Green Version] - Koivisto, M.; Degefa, M.; Ali, M.; Ekström, J.; Millar, J.; Lehtonen, M. Statistical Modeling of Aggregated Electricity Consumption and Distributed Wind Generation in Distribution Systems Using AMR Data. Electr. Power Syst. Res.
**2015**, 129, 217–226. [Google Scholar] [CrossRef] - Gao, X.; Li, X.; Zhao, B.; Ji, W.; Jing, X.; He, Y. Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection. Energies
**2019**, 12, 1140. [Google Scholar] [CrossRef][Green Version] - Jurado, S.; Nebot, À.; Mugica, F.; Avellana, N. Hybrid Methodologies for Electricity Load Forecasting: Entropy-Based Feature Selection with Machine Learning and Soft Comput. Techniques. Energy
**2015**, 86, 276–291. [Google Scholar] [CrossRef][Green Version] - Zhang, X. Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm. Energies
**2018**, 11, 1449. [Google Scholar] [CrossRef][Green Version] - Rabie, A.H.; Ali, S.H.; Ali, H.A.; Saleh, A.I. A Fog Based Load Forecasting Strategy for Smart Grids Using Big Electrical Data. Clust. Comput.
**2019**, 22, 241–270. [Google Scholar] [CrossRef] - Ke, K.; Hongbin, S.; Chengkang, Z.; Brown, C. Short-Term Electrical Load Forecasting Method Based on Stacked Auto-Encoding and GRU Neural Network. Evol. Intell.
**2019**, 12, 385–394. [Google Scholar] [CrossRef] - Chen, Q.; Xia, M.; Lu, T.; Jiang, X.; Liu, W.; Sun, Q. Short-Term Load Forecasting Based on Deep Learning for End-User Transformer Subject to Volatile Electric Heating Loads. IEEE Access
**2019**, 7, 162697–162707. [Google Scholar] [CrossRef] - Lu, S.; Lin, G.; Que, H.; Chen, L.; Liu, H.; Ye, C.; Yi, D. Electric Load Data Characterising and Forecasting Based on Trend Index and Auto-Encoders. J. Eng.
**2018**, 2018, 1915–1921. [Google Scholar] [CrossRef] - Xu, L.; Li, C.; Xie, X.; Zhang, G. Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting. Information
**2018**, 9, 165. [Google Scholar] [CrossRef][Green Version] - Zhang, D.; Tong, H.; Li, F.; Xiang, L.; Ding, X. An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model. Energies
**2020**, 13, 4875. [Google Scholar] [CrossRef] - Shao, X.; Kim, C.-S.; Sontakke, P. Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN–LSTM. Energies
**2020**, 13, 1881. [Google Scholar] [CrossRef][Green Version] - Lin, T.; Pan, Y.; Xue, G.; Song, J.; Qi, C. A Novel Hybrid Spatial-Temporal Attention-LSTM Model for Heat Load Prediction. IEEE Access
**2020**, 8, 159182–159195. [Google Scholar] [CrossRef] - Abedinia, O.; Amjady, N. Short-Term Load Forecast of Electrical Power System by Radial Basis Function Neural Network and New Stochastic Search Algorithm. Int. Trans. Electr. Energy Syst.
**2016**, 26, 1511–1525. [Google Scholar] [CrossRef] - Pîrjan, A.; Oprea, S.-V.; Căruțașu, G.; Petroșanu, D.-M.; Bâra, A.; Coculescu, C.; Pîrjan, A.; Oprea, S.-V.; Căruțașu, G.; Petroșanu, D.-M.; et al. Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers. Energies
**2017**, 10, 1727. [Google Scholar] [CrossRef][Green Version] - Amara, F.; Agbossou, K.; Dubé, Y.; Kelouwani, S.; Cardenas, A.; Bouchard, J. Household Electricity Demand Forecasting Using Adaptive Conditional Density Estimation. Energy Build.
**2017**, 156, 271–280. [Google Scholar] [CrossRef] - Protić, M.; Shamshirband, S.; Petković, D.; Abbasi, A.; Mat Kiah, M.L.; Unar, J.A.; Živković, L.; Raos, M. Forecasting of Consumers Heat Load in District Heating Systems Using the Support Vector Machine with a Discrete Wavelet Transform Algorithm. Energy
**2015**, 87, 343–351. [Google Scholar] [CrossRef] - Kong, Z.; Xia, Z.; Cui, Y.; Lv, H. Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine. Appl. Sci.
**2019**, 9, 4215. [Google Scholar] [CrossRef][Green Version] - Yukseltan, E.; Yucekaya, A.; Bilge, A.H. Hourly Electricity Demand Forecasting Using Fourier Analysis with Feedback. Energy Strategy Rev.
**2020**, 31, 100524. [Google Scholar] [CrossRef] - Guo, H.; Chen, Q.; Xia, Q.; Kang, C.; Zhang, X. A Monthly Electricity Consumption Forecasting Method Based on Vector Error Correction Model and Self-Adaptive Screening Method. Int. J. Electr. Power Energy Syst.
**2018**, 95, 427–439. [Google Scholar] [CrossRef] - Brożyna, J.; Mentel, G.; Szetela, B.; Strielkowski, W. Multi-Seasonality in the TBATS Model Using Demand for Electric Energy as a Case Study. Econ. Comput. Econ. Cybern. Stud. Res.
**2018**, 52, 229–246. [Google Scholar] [CrossRef] - Lakovic, M.; Pavlovic, I.; Banjac, M.; Jovic, M.; Mancic, M. Numerical Computation and Prediction of Electricity Consumption in Tobacco Industry. Facta Univ. Ser. Mech. Eng.
**2017**, 15, 457–465. [Google Scholar] [CrossRef][Green Version] - Yukseltan, E.; Yucekaya, A.; Bilge, A.H. Forecasting Electricity Demand for Turkey: Modeling Periodic Variations and Demand Segregation. Appl. Energy
**2017**, 193, 287–296. [Google Scholar] [CrossRef] - Trull, Ó.; García-Díaz, J.; Troncoso, A. Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies
**2019**, 12, 1083. [Google Scholar] [CrossRef][Green Version] - Dahl, M.; Brun, A.; Kirsebom, O.; Andresen, G. Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data. Energies
**2018**, 11, 1678. [Google Scholar] [CrossRef][Green Version] - Rego, L.; Sumaili, J.; Miranda, V.; Francês, C.; Silva, M.; Santana, Á. Mean Shift Densification of Scarce Data Sets in Short-Term Electric Power Load Forecasting for Special Days. Electr Eng
**2017**, 99, 881–898. [Google Scholar] [CrossRef] - Chang, C.-J.; Lin, J.-Y.; Chang, M.-J. Extended Modeling Procedure Based on the Projected Sample for Forecasting Short-Term Electricity Consumption. Adv. Eng. Inform.
**2016**, 30, 211–217. [Google Scholar] [CrossRef] - Wedeward, K.; Adkins, C.; Schaffer, S.; Smith, M.; Patel, A. Inventory of Load Models in Electric Power Systems via Parameter Estimation. Eng. Lett.
**2015**, 23, 9. [Google Scholar] - Stefanovic, A.; Gordic, D. Modeling Methodology of the Heating Energy Consumption and the Potential Reductions Due to Thermal Improvements of Staggered Block Buildings. Energy Build.
**2016**, 125, 244–253. [Google Scholar] [CrossRef] - Yan, C.; Wang, S.; Shan, K.; Lu, Y. A Simplified Analytical Model to Evaluate the Impact of Radiant Heat on Building Cooling Load. Appl. Therm. Eng.
**2015**, 77, 30–41. [Google Scholar] [CrossRef] - Wang, J.; Zhang, J.; Nie, J. An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism. Math. Probl. Eng.
**2016**, 2016, 8496971. [Google Scholar] [CrossRef] - Mousavi, S.M.; Mostafavi, E.S.; Hosseinpour, F. Towards Estimation of Electricity Demand Utilizing a Robust Multi-Gene Genetic Programming Technique. Energy Effic.
**2015**, 8, 1169–1180. [Google Scholar] [CrossRef] - Jawad, M.; Ali, S.M.; Khan, B.; Mehmood, C.A.; Farid, U.; Ullah, Z.; Usman, S.; Fayyaz, A.; Jadoon, J.; Tareen, N.; et al. Genetic Algorithm-Based Non-Linear Auto-Regressive with Exogenous Inputs Neural Network Short-Term and Medium-Term Uncertainty Modelling and Prediction for Electrical Load and Wind Speed. J. Eng.
**2018**, 2018, 721–729. [Google Scholar] [CrossRef] - Fan, G.-F.; Wang, A.; Hong, W.-C. Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting. Energies
**2018**, 11, 1625. [Google Scholar] [CrossRef][Green Version] - Ezugwu, A.E.; Adeleke, O.J.; Akinyelu, A.A.; Viriri, S. A Conceptual Comparison of Several Metaheuristic Algorithms on Continuous Optimisation Problems. Neural Comput. Applic
**2020**, 32, 6207–6251. [Google Scholar] [CrossRef] - Razavi, S.H.; Ahmadi, R.; Zahedi, A. Modeling, Simulation and Dynamic Control of Solar Assisted Ground Source Heat Pump to Provide Heating Load and DHW. Appl. Therm. Eng.
**2018**, 129, 127–144. [Google Scholar] [CrossRef] - Mosavi, A.; Salimi, M.; Faizollahzadeh Ardabili, S.; Rabczuk, T.; Shamshirband, S.; Varkonyi-Koczy, A.R. State of the Art of Machine Learning Models in Energy Syst, a Systematic Review. Energies
**2019**, 12, 1301. [Google Scholar] [CrossRef][Green Version] - Montuori, L.; Alcázar-Ortega, M.; Mansó-Borràs, I.; Vargas-Salgado, C. Communication Technologies in Smart Grids of Natural Gas: New Challenges. In Proceedings of the 2020 Global Congress on Electrical Engineering (GC-ElecEng 2020), Valencia, Spain, 4–6 September 2020; pp. 101–105. [Google Scholar]
- Stănişteanu, C. Smart Thermal Grids—A Review. Sci. Bull. Electr. Eng. Fac.
**2017**, 17, 36. [Google Scholar] [CrossRef][Green Version] - Švajlenka, J.; Kozlovská, M. Evaluation of the Efficiency and Sustainability of Timber-Based Construction. J. Clean. Prod.
**2020**, 259, 120835. [Google Scholar] [CrossRef] - Švajlenka, J.; Kozlovská, M. Effect of Accumulation Elements on the Energy Consumption of Wood Constructions. Energy Build.
**2019**, 198, 160–169. [Google Scholar] [CrossRef] - Belussi, L.; Barozzi, B.; Bellazzi, A.; Danza, L.; Devitofrancesco, A.; Fanciulli, C.; Ghellere, M.; Guazzi, G.; Meroni, I.; Salamone, F.; et al. A Review of Performance of Zero Energy Buildings and Energy Efficiency Solutions. J. Build. Eng.
**2019**, 25, 100772. [Google Scholar] [CrossRef] - Danza, L.; Belussi, L.; Salamone, F. A Multiple Linear Regression Approach to Correlate the Indoor Environmental Factors to the Global Comfort in a Zero-Energy Building. E3S Web Conf.
**2020**, 197, 04002. [Google Scholar] [CrossRef] - Dordonnat, V.; Pichavant, A.; Pierrot, A. GEFCom2014 Probabilistic Electric Load Forecasting Using Time Series and Semi-Parametric Regression Models. Int. J. Forecast.
**2016**, 32, 1005–1011. [Google Scholar] [CrossRef] - Xie, J.; Hong, T. GEFCom2014 Probabilistic Electric Load Forecasting: An Integrated Solution with Forecast Combination and Residual Simulation. Int. J. Forecast.
**2016**, 32, 1012–1016. [Google Scholar] [CrossRef] - Hong, T. GEFCom. 2017. Available online: http://www.drhongtao.com/gefcom/2017 (accessed on 5 May 2021).
- Liu, Z.; Wu, D.; Liu, Y.; Han, Z.; Lun, L.; Gao, J.; Jin, G.; Cao, G. Accuracy Analyses and Model Comparison of Machine Learning Adopted in Building Energy Consumption Prediction. Energy Explor. Exploit.
**2019**, 37, 1426–1451. [Google Scholar] [CrossRef][Green Version] - Kumar, K.P.; Saravanan, B. Recent Techniques to Model Uncertainties in Power Generation from Renewable Energy Sources and Loads in Microgrids – A Review. Renew. Sustain. Energy Rev.
**2017**, 71, 348–358. [Google Scholar] [CrossRef] - Wan Alwi, S.R.; Klemeš, J.J.; Varbanov, P.S. Cleaner Energy Planning, Management and Technologies: Perspectives of Supply-Demand Side and End-of-Pipe Management. J. Clean. Prod.
**2016**, 136, 1–13. [Google Scholar] [CrossRef] - Mat Daut, M.A.; Hassan, M.Y.; Abdullah, H.; Rahman, H.A.; Abdullah, M.P.; Hussin, F. Building Electrical Energy Consumption Forecasting Analysis Using Conventional and Artificial Intelligence Methods: A Review. Renew. Sustain. Energy Rev.
**2017**, 70, 1108–1118. [Google Scholar] [CrossRef] - Khuntia, S.R.; Rueda, J.L.; van der Meijden, M.A.M.M. Forecasting the Load of Electrical Power Systems in Mid- and Long-Term Horizons: A Review. IET Gener. Transm. Distrib.
**2016**, 10, 3971–3977. [Google Scholar] [CrossRef][Green Version] - Torriti, J. A Review of Time Use Models of Residential Electricity Demand. Renew. Sustain. Energy Rev.
**2014**, 37, 265–272. [Google Scholar] [CrossRef] - Yildiz, B.; Bilbao, J.I.; Sproul, A.B. A Review and Analysis of Regression and Machine Learning Models on Commercial Building Electricity Load Forecasting. Renew. Sustain. Energy Rev.
**2017**, 73, 1104–1122. [Google Scholar] [CrossRef] - Zhou, L.; Li, J.; Li, F.; Meng, Q.; Li, J.; Xu, X. Energy Consumption Model and Energy Effic. of Machine Tools: A Comprehensive Literature Review. J. Clean. Prod.
**2016**, 112, 3721–3734. [Google Scholar] [CrossRef] - Mahmud, K.; Town, G.E. A Review of Computer Tools for Modeling Electric Vehicle Energy Requirements and Their Impact on Power Distribution Networks. Appl. Energy
**2016**, 172, 337–359. [Google Scholar] [CrossRef] - Mavromatidis, L.E. A Review on Hybrid Optimization Algorithms to Coalesce Computational Morphogenesis with Interactive Energy Consumption Forecasting. Energy Build.
**2015**, 106, 192–202. [Google Scholar] [CrossRef] - Frayssinet, L.; Merlier, L.; Kuznik, F.; Hubert, J.-L.; Milliez, M.; Roux, J.-J. Modeling the Heating and Cooling Energy Demand of Urban Buildings at City Scale. Renew. Sustain. Energy Rev.
**2018**, 81, 2318–2327. [Google Scholar] [CrossRef][Green Version] - Deb, C.; Zhang, F.; Yang, J.; Lee, S.E.; Shah, K.W. A Review on Time Series Forecasting Techniques for Building Energy Consumption. Renew. Sustain. Energy Rev.
**2017**, 74, 902–924. [Google Scholar] [CrossRef] - Pengwei, S.; Xue, T.; Yan, W.; Shuai, D.; Jun, Z.; Qingsong, A.; Yongzhen, W. Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System. Energies
**2017**, 10, 1303. [Google Scholar] [CrossRef][Green Version] - Fumo, N.; Rafe Biswas, M.A. Regression Analysis for Prediction of Residential Energy Consumption. Renew. Sustain. Energy Rev.
**2015**, 47, 332–343. [Google Scholar] [CrossRef] - Ahmad, A.S.; Hassan, M.Y.; Abdullah, M.P.; Rahman, H.A.; Hussin, F.; Abdullah, H.; Saidur, R. A Review on Applications of ANN and SVM for Building Electrical Energy Consumption Forecasting. Renew. Sustain. Energy Rev.
**2014**, 33, 102–109. [Google Scholar] [CrossRef] - Ahmad, T.; Chen, H.; Guo, Y.; Wang, J. A Comprehensive Overview on the Data Driven and Large Scale Based Approaches for Forecasting of Building Energy Demand: A Review. Energy Build.
**2018**, 165, 301–320. [Google Scholar] [CrossRef] - Rafique, S.F.; Jianhua, Z. Energy Management System, Generation and Demand Predictors: A Review. IET Gener. Transm. Distrib.
**2017**, 12, 519–530. [Google Scholar] [CrossRef] - Shao, Z.; Chao, F.; Yang, S.-L.; Zhou, K.-L. A Review of the Decomposition Methodology for Extracting and Identifying the Fluctuation Characteristics in Electricity Demand Forecasting. Renew. Sustain. Energy Rev.
**2017**, 75, 123–136. [Google Scholar] [CrossRef] - Cabeza, L.F.; Palacios, A.; Serrano, S.; Ürge-Vorsatz, D.; Barreneche, C. Comparison of Past Projections of Global and Regional Primary and Final Energy Consumption with Historical Data. Renew. Sustain. Energy Rev.
**2018**, 82, 681–688. [Google Scholar] [CrossRef][Green Version] - Salisu, A.A.; Ayinde, T.O. Modeling Energy Demand: Some Emerging Issues. Renew. Sustain. Energy Rev.
**2016**, 54, 1470–1480. [Google Scholar] [CrossRef] - Fiot, J.-B.; Dinuzzo, F. Electricity Demand Forecasting by Multi-Task Learning. IEEE Trans. Smart Grid
**2018**, 9, 544–551. [Google Scholar] [CrossRef][Green Version] - He, Y.; Jiao, J.; Chen, Q.; Ge, S.; Chang, Y.; Xu, Y. Urban Long Term Electricity Demand Forecast Method Based on System Dynamics of the New Economic Normal: The Case of Tianjin. Energy
**2017**, 133, 9–22. [Google Scholar] [CrossRef] - Vu, D.H.; Muttaqi, K.M.; Agalgaonkar, A.P.; Bouzerdoum, A. Short-Term Electricity Demand Forecasting Using Autoregressive Based Time Varying Model Incorporating Representative Data Adjustment. Appl. Energy
**2017**, 205, 790–801. [Google Scholar] [CrossRef] - Alani, A.Y.; Osunmakinde, I.O.; Alani, A.Y.; Osunmakinde, I.O. Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes. Sustainability
**2017**, 9, 1972. [Google Scholar] [CrossRef][Green Version] - Tong, C.; Li, J.; Lang, C.; Kong, F.; Niu, J.; Rodrigues, J.J.P.C. An Efficient Deep Model for Day-Ahead Electricity Load Forecasting with Stacked Denoising Auto-Encoders. J. Parallel Distrib. Comput.
**2018**, 117, 267–273. [Google Scholar] [CrossRef] - Mokilane, P.; Galpin, J.; Yadavalli, V.S.S.; Debba, P.; Koen, R.; Sibiya, S. Density Forecasting for Long-Term Electricity Demand in South Africa Using Quantile Regression. South Afr. J. Econ. Manag. Sci.
**2018**, 21, a1757. [Google Scholar] [CrossRef] - Bikcora, C.; Verheijen, L.; Weiland, S. Density Forecasting of Daily Electricity Demand with ARMA-GARCH, CAViaR, and CARE Econometric Models. Sustain. Energy Grids Netw.
**2018**, 13, 148–156. [Google Scholar] [CrossRef] - Nadimi, R.; Tokimatsu, K. Modeling of Quality of Life in Terms of Energy and Electricity Consumption. Appl. Energy
**2018**, 212, 1282–1294. [Google Scholar] [CrossRef] - Yan, Q.; Qin, C.; Nie, M.; Yang, L. Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain. Math. Probl. Eng.
**2018**, 2018, 4671850. [Google Scholar] [CrossRef] - Li, J.; Chen, L.; Xiang, Y.; Li, J.; Peng, D. Influencing Factors and Development Trend Analysis of China Electric Grid Investment Demand Based on a Panel Co-Integration Model. Sustainability
**2018**, 10, 256. [Google Scholar] [CrossRef][Green Version] - Yang, L.; Yang, H.; Liu, H. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting. Sustainability
**2018**, 10, 217. [Google Scholar] [CrossRef][Green Version] - Yamazaki, T.; Wakao, S.; Ito, H.; Sano, T. Prediction Interval Estimation of Demand Curve in Electric Power Distribution System. Electr. Eng. Jpn.
**2018**, 202, 12–23. [Google Scholar] [CrossRef] - Yeom, C.-U.; Kwak, K.-C. Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation. Energies
**2017**, 10, 1613. [Google Scholar] [CrossRef] - Duan, Q.; Liu, J.; Zhao, D. Short Term Electric Load Forecasting Using an Automated System of Model Choice. Int. J. Electr. Power Energy Syst.
**2017**, 91, 92–100. [Google Scholar] [CrossRef] - Li, Z.; Hurn, A.S.; Clements, A.E. Forecasting Quantiles of Day-Ahead Electricity Load. Energy Econ.
**2017**, 67, 60–71. [Google Scholar] [CrossRef][Green Version] - Zhang, C.; Zhou, K.; Yang, S.; Shao, Z. Exploring the Transformation and Upgrading of China’s Economy Using Electricity Consumption Data: A VAR–VEC Based Model. Phys. A Stat. Mech. Its Appl.
**2017**, 473, 144–155. [Google Scholar] [CrossRef] - De Cabral, J.A.; Legey, L.F.L.; de Freitas Cabral, M.V. Electricity Consumption Forecasting in Brazil: A Spatial Econometrics Approach. Energy
**2017**, 126, 124–131. [Google Scholar] [CrossRef] - Wijayapala, W.D.A.S.; Siyambalapitiya, T.; Jayasekara, I.N. Developing a Mathematical Model Based on Weather Parameters to Predict the Daily Demand for Electricity. Eng. J. Inst. Eng. Sri Lanka
**2017**, 50, 49. [Google Scholar] [CrossRef][Green Version] - Sigauke, C.; Bere, A. Modelling Non-Stationary Time Series Using a Peaks over Threshold Distribution with Time Varying Covariates and Threshold: An Application to Peak Electricity Demand. Energy
**2017**, 119, 152–166. [Google Scholar] [CrossRef] - Li, Y.; Bao, Y.-Q.; Yang, B.; Chen, C.; Ruan, W. Modification Method to Deal with the Accumulation Effects for Summer Daily Electric Load Forecasting. Int. J. Electr. Power Energy Syst.
**2015**, 73, 913–918. [Google Scholar] [CrossRef] - Bernardi, M.; Petrella, L. Multiple Seasonal Cycles Forecasting Model: The Italian Electricity Demand. Statistical Methods & Applications
**2015**, 24, 671–695. [Google Scholar] [CrossRef] - Shao, Z.; Gao, F.; Zhang, Q.; Yang, S.-L. Multivariate Statistical and Similarity Measure Based Semiparametric Modeling of the Probability Distribution: A Novel Approach to the Case Study of Mid-Long Term Electricity Consumption Forecasting in China. Appl. Energy
**2015**, 156, 502–518. [Google Scholar] [CrossRef] - Yang, Z. Electric Load Movement Evaluation and Forecasting Based on the Fourier-Series Model Extend in the Least-Squares Sense. J. Control. Autom. Electr. Syst.
**2015**, 26, 430–440. [Google Scholar] [CrossRef] - Chitsaz, H.; Shaker, H.; Zareipour, H.; Wood, D.; Amjady, N. Short-Term Electricity Load Forecasting of Buildings in Microgrids. Energy Build.
**2015**, 99, 50–60. [Google Scholar] [CrossRef] - Koprinska, I.; Rana, M.; Agelidis, V.G. Correlation and Instance Based Feature Selection for Electricity Load Forecasting. Knowl. Based Syst.
**2015**, 82, 29–40. [Google Scholar] [CrossRef] - Launay, T.; Philippe, A.; Lamarche, S. Construction of an Informative Hierarchical Prior for a Small Sample with the Help of Historical Data and Application to Electricity Load Forecasting. Test
**2015**, 24, 361–385. [Google Scholar] [CrossRef][Green Version] - Efendi, R.; Ismail, Z.; Deris, M.M. A New Linguistic Out-Sample Approach of Fuzzy Time Series for Daily Forecasting of Malaysian Electricity Load Demand. Appl. Soft Comput.
**2015**, 28, 422–430. [Google Scholar] [CrossRef] - Laouafi, A.; Mordjaoui, M.; Dib, D. One-Hour Ahead Electric Load Forecasting Using Neuro-Fuzzy System in a Parallel Approach. In Computational Intelligence Applications in Modeling and Control; Azar, A.T., Vaidyanathan, S., Eds.; Springer International Publishing: Cham, Switzerland, 2015; Volume 575, pp. 95–121. ISBN 978-3-319-11016-5. [Google Scholar]
- Cui, H.; Peng, X. Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model. Math. Probl. Eng.
**2015**, 2015, 589374. [Google Scholar] [CrossRef][Green Version] - Castelli, M.; Vanneschi, L.; De Felice, M. Forecasting Short-Term Electricity Consumption Using a Semantics-Based Genetic Programming Framework: The South Italy Case. Energy Econ.
**2015**, 47, 37–41. [Google Scholar] [CrossRef] - Panklib, K.; Prakasvudhisarn, C.; Khummongkol, D. Electricity Consumption Forecasting in Thailand Using an Artificial Neural Network and Multiple Linear Regression. Energy Sources Part B Econ. Plan. Policy
**2015**, 10, 427–434. [Google Scholar] [CrossRef] - Simões, M.D.; Klotzle, M.C.; Pinto, A.C.F.; Gomes, L.L. Non Linear Models and the Load of an Electricity Distributor. Int. J. Energy Sect. Manag.
**2015**, 9, 38–56. [Google Scholar] [CrossRef] - Sigauke, C.; Chikobvu, D. Estimation of Extreme Inter-Day Changes to Peak Electricity Demand Using Markov Chain Analysis: A Comparative Analysis with Extreme Value Theory. J. Energy South. Afr.
**2017**, 28, 57–67. [Google Scholar] [CrossRef] - Kaytez, F.; Taplamacioglu, M.C.; Cam, E.; Hardalac, F. Forecasting Electricity Consumption: A Comparison of Regression Analysis, Neural Networks and Least Squares Support Vector Machines. Int. J. Electr. Power Energy Syst.
**2015**, 67, 431–438. [Google Scholar] [CrossRef] - Herui, C.; Xu, P.; Yupei, M. Electric Load Forecast Using Combined Models with HP Filter-SARIMA and ARMAX Optimized by Regression Analysis Algorithm. Math. Probl. Eng.
**2015**, 2015, 386925. [Google Scholar] [CrossRef][Green Version] - Vera, V.D.G. Forecasting Electricity Demand for Small Colombian Populations. Cuad. Act.
**2015**, 7, 111–120. [Google Scholar] - Zolfaghari, M.; Sahabi, B. A Hybrid Approach to Model and Forecast the Electricity Consumption by NeuroWavelet and ARIMAX-GARCH Models. Energy Effic.
**2019**, 12, 2099–2122. [Google Scholar] [CrossRef] - Cui, X.; Zhao, W. Study of the Modified Logistic Model of Chinese Electricity Consumption Based on the Change of the GDP Growth Rate under the Economic New Normal. Math. Probl. Eng.
**2019**, 2019, 3901821. [Google Scholar] [CrossRef] - Liu, Y.; Zhao, J.; Liu, J.; Chen, Y.; Ouyang, H. Regional Midterm Electricity Demand Forecasting Based on Economic, Weather, Holiday, and Events Factors. IEEJ Trans. Electr. Electron. Eng.
**2020**, 15, 225–234. [Google Scholar] [CrossRef] - Jornaz, A.; Samaranayake, V.A. A Multi-Step Approach to Modeling the 24-Hour Daily Profiles of Electricity Load Using Daily Splines. Energies
**2019**, 12, 4169. [Google Scholar] [CrossRef][Green Version] - Tena García, J.L.; Cadenas Calderón, E.; Rangel Heras, E.; Morales Ontiveros, C. Generating Electrical Demand Time Series Applying SRA Technique to Complement NAR and SARIMA Models. Energy Effic.
**2019**, 12, 1751–1769. [Google Scholar] [CrossRef] - Ullah, G. Medium Term Electric Load Forecasting Using Lancsoz Bidiagonalization with Singular Value Decomposition. J. Mech. Contin. Math. Sci.
**2019**, 14, 349–360. [Google Scholar] [CrossRef][Green Version] - Liu, P.; Zheng, P.; Chen, Z. Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting. Energies
**2019**, 12, 2445. [Google Scholar] [CrossRef][Green Version] - Laurinec, P.; Lucká, M. Interpretable Multiple Data Streams Clustering with Clipped Streams Representation for the Improvement of Electricity Consumption Forecasting. Data Min. Knowl. Discov.
**2019**, 33, 413–445. [Google Scholar] [CrossRef] - Zhu, J.; Luo, T.; Chen, J.; Xia, Y.; Wang, C.; Liu, M. Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework. IEEE Access
**2019**, 7, 121145–121155. [Google Scholar] [CrossRef] - Song, X.; Liang, G.; Li, C.; Chen, W. Electricity Consumption Prediction for Xinjiang Electric Energy Replacement. Math. Probl. Eng.
**2019**, 2019, 3262591. [Google Scholar] [CrossRef][Green Version] - Khuntia, S.; Rueda, J.; van der Meijden, M. Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model. Energies
**2018**, 11, 3308. [Google Scholar] [CrossRef][Green Version] - Zhao, L.; Zhou, X. Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator. Symmetry
**2018**, 10, 693. [Google Scholar] [CrossRef][Green Version] - Wang, J.; Zhao, J.; Li, H. The Electricity Consumption and Economic Growth Nexus in China: A Bootstrap Seemingly Unrelated Regression Estimator Approach. Comput. Econ.
**2018**, 52, 1195–1211. [Google Scholar] [CrossRef] - Yang, T.; Liu, T.; Chen, J.; Yan, S.; Hui, S.Y.R. Dynamic Modular Modeling of Smart Loads Associated With Electric Springs and Control. IEEE Trans. Power Electron.
**2018**, 33, 10071–10085. [Google Scholar] [CrossRef] - Salkuti, S.R. Short-Term Electrical Load Forecasting Using Radial Basis Function Neural Networks Considering Weather Factors. Electr. Eng.
**2018**, 100, 1985–1995. [Google Scholar] [CrossRef] - Jung, H.-W.; Song, K.-B.; Park, J.-D.; Park, R.-J. Very Short-Term Electric Load Forecasting for Real-Time Power System Operation. J. Electr. Eng. Technol.
**2018**, 13, 1419–1424. [Google Scholar] - Ragu, V.; Yang, S.-W.; Chae, K.; Park, J.; Shin, C.; Yang, S.Y.; Cho, Y. Analysis and Forecasting of Electric Power Energy Consumption in IoT Environments. Int. J. Grid Distrib. Comput.
**2018**, 11, 1–14. [Google Scholar] [CrossRef] - Bedi, J.; Toshniwal, D. Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting. IEEE Access
**2018**, 6, 49144–49156. [Google Scholar] [CrossRef] - Laurinec, P.; Lucká, M. Clustering-Based Forecasting Method for Individual Consumers Electricity Load Using Time Series Representations. Open Comput. Sci.
**2018**, 8, 38–50. [Google Scholar] [CrossRef] - Salehi, M.; Qeidari, H.S.; Asgari, A. The Impact of Targeted Subsidies on Electricity Consumption, Sale, Receivables Collection and Operating Cash Flow. Int. J. Soc. Econ.
**2017**, 44, 505–520. [Google Scholar] [CrossRef] - Hock-Eam, L.; Chee-Yin, Y. How Accurate Is TNB’s Electricity Demand Forecast? Malays. J. Math. Sci.
**2016**, 10, 79–90. [Google Scholar] - Daraghmi, Y.; Daraghmi, E.Y.; Alsaadi, S.; Eleyan, D. Accurate and Time-efficient Negative Binomial Linear Model for Electric Load Forecasting in IoE. Trans. Emerg. Telecommun. Technol.
**2019**, 37, e4103. [Google Scholar] [CrossRef] - Hamzaçebi, C.; Es, H.A.; Çakmak, R. Forecasting of Turkey’s Monthly Electricity Demand by Seasonal Artificial Neural Network. Neural Comput. Appl.
**2019**, 31, 2217–2231. [Google Scholar] [CrossRef] - Ayvaz, B.; Kusakci, A.O. Electricity Consumption Forecasting for Turkey with Nonhomogeneous Discrete Grey Model. Energy Sources Part B Econ. Plan. Policy
**2017**, 12, 260–267. [Google Scholar] [CrossRef] - Dönmez, A.H.; Karakoyun, Y.; Yumurtaci, Z. Electricity Demand Forecast of Turkey Based on Hydropower and Windpower Potential. Energy Sources Part B Econ. Plan. Policy
**2017**, 12, 85–90. [Google Scholar] [CrossRef] - Duque-Pintor, F.; Fernández-Gómez, M.; Troncoso, A.; Martínez-Álvarez, F. A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series. Energies
**2016**, 9, 752. [Google Scholar] [CrossRef][Green Version] - Suryani, E.; Agus Hendrawan, R.; Dewi, L.P. Dynamic Simulation Model Of Electricity Energy Demand And Power Plant Capacity Planning In Madura. J. Teknol.
**2016**, 78, 367–372. [Google Scholar] [CrossRef][Green Version] - Song, Y.; Guerrero, J.M.; Shen, Z.; Wu, X. Model-Independent Approach for Short-Term Electric Load Forecasting with Guaranteed Error Convergence. IET Control. Theory Appl.
**2016**, 10, 1365–1373. [Google Scholar] [CrossRef] - Wang, P.; Liu, B.; Hong, T. Electric Load Forecasting with Recency Effect: A Big Data Approach. Int. J. Forecast.
**2016**, 32, 585–597. [Google Scholar] [CrossRef][Green Version] - Clements, A.E.; Hurn, A.S.; Li, Z. Forecasting Day-Ahead Electricity Load Using a Multiple Equation Time Series Approach. Eur. J. Oper. Res.
**2016**, 251, 522–530. [Google Scholar] [CrossRef][Green Version] - Melo, J.D.; Carreno, E.M.; Padilha-Feltrin, A.; Minussi, C.R. Grid-Based Simulation Method for Spatial Electric Load Forecasting Using Power-Law Distribution with Fractal Exponent. Int. Trans. Electr. Energy Syst.
**2016**, 26, 1339–1357. [Google Scholar] [CrossRef] - Do, L.P.C.; Lin, K.-H.; Molnár, P. Electricity Consumption Modelling: A Case of Germany. Econ. Model.
**2016**, 55, 92–101. [Google Scholar] [CrossRef] - Ertugrul, Ö.F. Forecasting Electricity Load by a Novel Recurrent Extreme Learning Machines Approach. Int. J. Electr. Power Energy Syst.
**2016**, 78, 429–435. [Google Scholar] [CrossRef] - Taylor, J.W.; Roberts, M.B. Forecasting Frequency-Corrected Electricity Demand to Support Frequency Control. IEEE Trans. Power Syst.
**2016**, 31, 1925–1932. [Google Scholar] [CrossRef] - Pérez-García, J.; Moral-Carcedo, J. Analysis and Long Term Forecasting of Electricity Demand Trough a Decomposition Model: A Case Study for Spain. Energy
**2016**, 97, 127–143. [Google Scholar] [CrossRef] - Hussain, A.; Rahman, M.; Memon, J.A. Forecasting Electricity Consumption in Pakistan: The Way Forward. Energy Policy
**2016**, 90, 73–80. [Google Scholar] [CrossRef] - Alves, A.C.; Yang, R.L.; Tiepolo, G.M. Projection of the Demand of Electricity in the State of Paraná for 2050 and Proposal of Complementarity of the Electrical Matrix through the Solar Photovoltaic Source. Braz. Arch. Biol. Technol.
**2018**, 61, 1–10. [Google Scholar] [CrossRef] - Zhu, X.; Yang, S.; Lin, J.; Wei, Y.-M.; Zhao, W. Forecasting China’s Electricity Demand up to 2030: A Linear Model Selection System. J. Model. Manag.
**2018**, 13, 570–586. [Google Scholar] [CrossRef] - Cheng, Q.; Yan, Y.; Liu, S.; Yang, C.; Chaoui, H.; Alzayed, M. Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling. Energies
**2020**, 13, 6489. [Google Scholar] [CrossRef] - Behm, C. How to Model European Electricity Load Profiles Using Artificial Neural Networks. Appl. Energy
**2020**, 17, 115564. [Google Scholar] [CrossRef] - Zhao, F.; Ding, J.; Zhang, S.; Luan, G.; Song, L.; Peng, Z.; Du, Q.; Xie, Z. Estimating Rural Electric Power Consumption Using NPP-VIIRS Night-Time Light, Toponym and POI Data in Ethnic Minority Areas of China. Remote. Sens.
**2020**, 12, 2836. [Google Scholar] [CrossRef] - Jung, S.-M.; Park, S.; Jung, S.-W.; Hwang, E. Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities. Sustainability
**2020**, 12, 6364. [Google Scholar] [CrossRef] - Almazrouee, A.I.; Almeshal, A.M.; Almutairi, A.S.; Alenezi, M.R.; Alhajeri, S.N. Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt–Winters Models. Appl. Sci.
**2020**, 10, 5627. [Google Scholar] [CrossRef] - Mokhov, V.G.; Demyanenko, T.S. A long-term forecasting model of electricity consumption volume on the example of ups of the ural with the help of harmonic analysis of a time series. Вeстник Южнo-Урaльскoгo Гoсудaрствeннoгo Унивeрситeтa. Сeрия «Мaтeмaтичeскoe Мoдeлирoвaниe И Прoгрaммирoвaниe»
**2020**, 13, 80–85. [Google Scholar] [CrossRef] - Elkamel, M.; Schleider, L.; Pasiliao, E.L.; Diabat, A.; Zheng, Q.P. Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study. Energies
**2020**, 13, 3996. [Google Scholar] [CrossRef] - Chen, Y.-T.; Sun, E.W.; Lin, Y.-B. Machine Learning with Parallel Neural Networks for Analyzing and Forecasting Electricity Demand. Comput. Econ.
**2020**, 56, 569–597. [Google Scholar] [CrossRef] - Hoori, A.O.; Kazzaz, A.A.; Khimani, R.; Motai, Y.; Aved, A.J. Electric Load Forecasting Model Using a Multicolumn Deep Neural Networks. IEEE Trans. Ind. Electron.
**2020**, 67, 6473–6482. [Google Scholar] [CrossRef] - Caro, E.; Juan, J. Short-Term Load Forecasting for Spanish Insular Electric Systems. Energies
**2020**, 13, 3645. [Google Scholar] [CrossRef] - Salat, H.; Smoreda, Z.; Schläpfer, M. A Method to Estimate Population Densities and Electricity Consumption from Mobile Phone Data in Developing Countries. PLoS ONE
**2020**, 15, e0235224. [Google Scholar] [CrossRef] [PubMed] - Zhao, C.; Wan, C.; Song, Y.; Cao, Z. Optimal Nonparametric Prediction Intervals of Electricity Load. IEEE Trans. Power Syst.
**2020**, 35, 2467–2470. [Google Scholar] [CrossRef] - Houimli, R. Short-Term Electric Load Forecasting in Tunisia Using Artificial Neural Networks. Energy Syst.
**2020**, 11, 357–375. [Google Scholar] [CrossRef] - Kalimoldayev, M.; Drozdenko, A.; Koplyk, I.; Marinich, T.; Abdildayeva, A.; Zhukabayeva, T. Analysis of Modern Approaches for the Prediction of Electric Energy Consumption. Open Eng.
**2020**, 10, 350–361. [Google Scholar] [CrossRef] - Dudic, B.; Smolen, J.; Kovac, P.; Savkovic, B.; Dudic, Z. Electricity Usage Efficiency and Electricity Demand Modeling in the Case of Germany and the UK. Appl. Sci.
**2020**, 10, 2291. [Google Scholar] [CrossRef][Green Version] - Trull, Ó.; García-Díaz, J.C.; Troncoso, A. Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain. Appl. Sci.
**2020**, 10, 2630. [Google Scholar] [CrossRef][Green Version] - Xu, X. A Hybrid Transfer Learning Model for Short-Term Electric Load Forecasting. Electr. Eng.
**2020**, 102, 1371–1381. [Google Scholar] [CrossRef] - Elias, I.; Martinez, D.I.; Muñiz, S.; Balcazar, R.; Garcia, E.; Juarez, C.F. Hessian with Mini-Batches for Electrical Demand Prediction. Appl. Sci.
**2020**, 10, 2036. [Google Scholar] [CrossRef][Green Version] - Jain, R. A Modified Fuzzy Logic Relation-Based Approach for Electricity Consumption Forecasting in India. Int. J. Fuzzy Syst.
**2020**, 22, 15. [Google Scholar] [CrossRef] - Sigauke, C.; Nemukula, M.M. Modelling Extreme Peak Electricity Demand during a Heatwave Period: A Case Study. Energy Syst.
**2020**, 11, 139–161. [Google Scholar] [CrossRef] - Matsuo, Y.; Oyama, T. Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique. J. Oper. Res. Soc. China
**2020**, 8, 655–667. [Google Scholar] [CrossRef] - Danandeh Mehr, A.; Bagheri, F.; Safari, M.J.S. Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree. Gazi Univ. J. Sci.
**2020**, 33, 62–72. [Google Scholar] [CrossRef] - Li, Y.; Jones, B. The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand. IEEE Trans. Power Syst.
**2020**, 35, 128–139. [Google Scholar] [CrossRef] - Kuusela, P.; Norros, I.; Reittu, H.; Piira, K. Hierarchical Multiplicative Model for Characterizing Residential Electricity Consumption. J. Energy Eng.
**2018**, 144, 04018023. [Google Scholar] [CrossRef] - El-Baz, W.; Tzscheutschler, P. Short-Term Smart Learning Electrical Load Prediction Algorithm for Home Energy Management Systems. Appl. Energy
**2015**, 147, 10–19. [Google Scholar] [CrossRef] - Van der Meer, D.W.; Shepero, M.; Svensson, A.; Widén, J.; Munkhammar, J. Probabilistic Forecasting of Electricity Consumption, Photovoltaic Power Generation and Net Demand of an Individual Building Using Gaussian Processes. Appl. Energy
**2018**, 213, 195–207. [Google Scholar] [CrossRef] - Rahman, A.; Srikumar, V.; Smith, A.D. Predicting Electricity Consumption for Commercial and Residential Buildings Using Deep Recurrent Neural Networks. Appl. Energy
**2018**, 212, 372–385. [Google Scholar] [CrossRef] - Bartolozzi, A.; Favuzza, S.; Ippolito, M.; La Cascia, D.; Riva Sanseverino, E.; Zizzo, G.; Bartolozzi, A.; Favuzza, S.; Ippolito, M.G.; La Cascia, D.; et al. A New Platform for Automatic Bottom-Up Electric Load Aggregation. Energies
**2017**, 10, 1682. [Google Scholar] [CrossRef][Green Version] - Motlagh, O.; Grozev, G.; Wang, C.-H.; James, M. A Neural Approach for Estimation of per Capita Electricity Consumption Due to Age and Income. Neural Comput. Appl.
**2017**, 28, 1747–1761. [Google Scholar] [CrossRef] - Sun, M.; Konstantelos, I.; Strbac, G. C-Vine Copula Mixture Model for Clustering of Residential Electrical Load Pattern Data. IEEE Trans. Power Syst.
**2017**, 32, 2382–2393. [Google Scholar] [CrossRef][Green Version] - Gomez, J.A.; Anjos, M.F. Power Capacity Profile Estimation for Building Heating and Cooling in Demand-Side Management. Appl. Energy
**2017**, 191, 492–501. [Google Scholar] [CrossRef][Green Version] - Kleebrang, W.; Bunditsakulchai, P.; Wangjiraniran, W. Household Electricity Demand Forecast and Energy Savings Potential for Vientiane, Lao PDR. Int. J. Sustain. Energy
**2017**, 36, 344–367. [Google Scholar] [CrossRef] - Qiu, Y.; Xing, B.; Wang, Y.D. Prepaid electricity plan and electricity consumption behavior: Prepaid electricity plan. Contemp. Econ. Policy
**2017**, 35, 125–142. [Google Scholar] [CrossRef] - Alibabaei, N.; Fung, A.S.; Raahemifar, K.; Moghimi, A. Effects of Intelligent Strategy Planning Models on Residential HVAC System Energy Demand and Cost during the Heating and Cooling Seasons. Appl. Energy
**2017**, 185, 29–43. [Google Scholar] [CrossRef] - Tsai, C.-L.; Chen, W.T.; Chang, C.-S. Polynomial-Fourier Series Model for Analyzing and Predicting Electricity Consumption in Buildings. Energy Build.
**2016**, 127, 301–312. [Google Scholar] [CrossRef] - Fischer, D.; Wolf, T.; Scherer, J.; Wille-Haussmann, B. A Stochastic Bottom-up Model for Space Heating and Domestic Hot Water Load Profiles for German Households. Energy Build.
**2016**, 124, 120–128. [Google Scholar] [CrossRef] - Verdejo, H.; Awerkin, A.; Saavedra, E.; Kliemann, W.; Vargas, L. Stochastic Modeling to Represent Wind Power Generation and Demand in Electric Power System Based on Real Data. Appl. Energy
**2016**, 173, 283–295. [Google Scholar] [CrossRef] - Torrini, F.C.; Souza, R.C.; Cyrino Oliveira, F.L.; Moreira Pessanha, J.F. Long Term Electricity Consumption Forecast in Brazil: A Fuzzy Logic Approach. Socio Econ. Plan. Sci.
**2016**, 54, 18–27. [Google Scholar] [CrossRef] - Reade, S.; Zewotir, T.; North, D. Modelling Household Electricity Consumption in EThekwini Municipality. J. Energy South. Afr.
**2016**, 27, 38–49. [Google Scholar] [CrossRef][Green Version] - Kipping, A.; Trømborg, E. Modeling and Disaggregating Hourly Electricity Consumption in Norwegian Dwellings Based on Smart Meter Data. Energy Build.
**2016**, 118, 350–369. [Google Scholar] [CrossRef] - Tascikaraoglu, A.; Sanandaji, B.M. Short-Term Residential Electric Load Forecasting: A Compressive Spatio-Temporal Approach. Energy Build.
**2016**, 111, 380–392. [Google Scholar] [CrossRef] - Burger, E.M.; Moura, S.J. Gated Ensemble Learning Method for Demand-Side Electricity Load Forecasting. Energy Build.
**2015**, 109, 23–34. [Google Scholar] [CrossRef] - Shen, X.; Han, Y.; Zhu, S.; Zheng, J.; Li, Q.; Nong, J. Comprehensive Power-Supply Planning for Active Distribution System Considering Cooling, Heating and Power Load Balance. J. Mod. Power Syst. Clean Energy
**2015**, 3, 485–493. [Google Scholar] [CrossRef][Green Version] - Kouzelis, K.; Tan, Z.H.; Bak-Jensen, B.; Pillai, J.R.; Ritchie, E. Estimation of Residential Heat Pump Consumption for Flexibility Market Applications. IEEE Trans. Smart Grid
**2015**, 6, 1852–1864. [Google Scholar] [CrossRef] - Göb, R.; Lurz, K.; Pievatolo, A. More Accurate Prediction Intervals for Exponential Smoothing with Covariates with Applications in Electrical Load Forecasting and Sales Forecasting: Prediction Intervals for Exponential Smoothing with Covariates. Qual. Reliab. Eng. Int.
**2015**, 31, 669–682. [Google Scholar] [CrossRef] - Milani, A.; Camarda, C.; Savoldi, L. A Simplified Model for the Electrical Energy Consumption of Washing Machines. J. Build. Eng.
**2015**, 2, 69–76. [Google Scholar] [CrossRef] - Morris, P.; Vine, D.; Buys, L. Application of a Bayesian Network Complex System Model to a Successful Community Electricity Demand Reduction Program. Energy
**2015**, 84, 63–74. [Google Scholar] [CrossRef][Green Version] - Garulli, A.; Paoletti, S.; Vicino, A. Models and Techniques for Electric Load Forecasting in the Presence of Demand Response. IEEE Trans. Control. Syst. Technol.
**2015**, 23, 1087–1097. [Google Scholar] [CrossRef] - Palacios-Garcia, E.J.; Chen, A.; Santiago, I.; Bellido-Outeiriño, F.J.; Flores-Arias, J.M.; Moreno-Munoz, A. Stochastic Model for Lighting’s Electricity Consumption in the Residential Sector. Impact of Energy Saving Actions. Energy Build.
**2015**, 89, 245–259. [Google Scholar] [CrossRef] - Hsiao, Y. Household Electricity Demand Forecast Based on Context Information and User Daily Schedule Analysis From Meter Data. IEEE Trans. Ind. Inform.
**2015**, 11, 33–43. [Google Scholar] [CrossRef] - Zheng, G.; Zhang, L. The Electrical Load Forecasting Base on an Optimal Selection Method of Multiple Models in DSM. Int. J. Online Eng.
**2015**, 11, 34. [Google Scholar] [CrossRef][Green Version] - Longe, O.M.; Ouahada, K.; Ferreira, H.C.; Rimer, S. Consumer Preference Electricity Usage Plan for Demand Side Management in the Smart Grid. SAIEE Afr. Res. J.
**2017**, 108, 174–184. [Google Scholar] [CrossRef][Green Version] - Pamuk, N. Empirical Analysis of Causal Relationship between Electricity Production and Consumption Demand in Turkey Using Cobb-Douglas Model. Politek. Derg.
**2016**, 19, 415–420. [Google Scholar] - Cui, G.; Liu, B.; Luan, W.; Yu, Y. Estimation of Target Appliance Electricity Consumption Using Background Filtering. IEEE Trans. Smart Grid
**2019**, 10, 5920–5929. [Google Scholar] [CrossRef] - Causone, F.; Carlucci, S.; Ferrando, M.; Marchenko, A.; Erba, S. A Data-Driven Procedure to Model Occupancy and Occupant-Related Electric Load Profiles in Residential Buildings for Energy Simulation. Energy Build.
**2019**, 202, 109342. [Google Scholar] [CrossRef] - Le, T.; Vo, M.T.; Vo, B.; Hwang, E.; Rho, S.; Baik, S.W. Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM. Appl. Sci.
**2019**, 9, 4237. [Google Scholar] [CrossRef][Green Version] - Laurinec, P.; Lóderer, M.; Lucká, M.; Rozinajová, V. Density-Based Unsupervised Ensemble Learning Methods for Time Series Forecasting of Aggregated or Clustered Electricity Consumption. J. Intell. Inf. Syst.
**2019**, 53, 219–239. [Google Scholar] [CrossRef] - Cordova, J.; Konila Sriram, L.M.; Kocatepe, A.; Zhou, Y.; Ozguven, E.E.; Arghandeh, R. Combined Electricity and Traffic Short-Term Load Forecasting Using Bundled Causality Engine. IEEE Trans. Intell. Transp. Syst.
**2019**, 20, 3448–3458. [Google Scholar] [CrossRef] - Rushman, J.F.; Thanarak, P.; Artkla, S. Electrical Power Demand Assessment of a Rural Community and the Forecast of Demand Growth for Rural Electrification in Ghana. Int. Energy J.
**2019**, 19, 177–188. [Google Scholar] - Gatarić, P.; Širok, B.; Hočevar, M.; Novak, L. Modeling of Heat Pump Tumble Dryer Energy Consumption and Drying Time. Dry. Technol.
**2019**, 37, 1396–1404. [Google Scholar] [CrossRef] - Kung, F.; Frank, S.; Pless, S.; Judkoff, R. Meter-Based Synthesis of Equipment Schedules for Improved Models of Electrical Demand in Multifamily Buildings. J. Build. Perform. Simul.
**2019**, 12, 388–403. [Google Scholar] [CrossRef] - Liu, Y.; Sun, Y.; Li, B. A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding. Information
**2019**, 10, 224. [Google Scholar] [CrossRef][Green Version] - Yang, C.C.; Soh, C.S.; Yap, V.V. A Systematic Approach in Load Disaggregation Utilizing a Multi-Stage Classification Algorithm for Consumer Electrical Appliances Classification. Front. Energy
**2019**, 13, 386–398. [Google Scholar] [CrossRef] - Lin, J.; Zhu, K.; Liu, Z.; Lieu, J.; Tan, X. Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation. Energies
**2019**, 12, 2220. [Google Scholar] [CrossRef][Green Version] - Sobhani, M.; Campbell, A.; Sangamwar, S.; Li, C.; Hong, T. Combining Weather Stations for Electric Load Forecasting. Energies
**2019**, 12, 1510. [Google Scholar] [CrossRef][Green Version] - Kushiro, N.; Fukuda, A.; Kawatsu, M.; Mega, T. Predict Electric Power Demand with Extended Goal Graph and Heterogeneous Mixture Modeling. Information
**2019**, 10, 134. [Google Scholar] [CrossRef][Green Version] - Cardot, H.; De Moliner, A.; Goga, C. Estimation of Total Electricity Consumption Curves by Sampling in a Finite Population When Some Trajectories Are Partially Unobserved. Can. J. Stat.
**2019**, 47, 65–89. [Google Scholar] [CrossRef][Green Version] - Kim, J.-Y.; Cho, S.-B. Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder. Energies
**2019**, 12, 739. [Google Scholar] [CrossRef][Green Version] - Gerossier, A.; Girard, R.; Bocquet, A.; Kariniotakis, G. Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges. Energies
**2018**, 11, 3503. [Google Scholar] [CrossRef][Green Version] - Ruiz-Vásquez, S.; Roldán, C.; Cheng, V. Development of a Modeling Tool for Simulating Electricity Demand and on Site PV Power Production in High Time Resolution: Applications in Costa Rica. Rev. Tecnol. En Marcha
**2019**, 31, 48–68. [Google Scholar] [CrossRef] - Teeraratkul, T.; O’Neill, D.; Lall, S. Shape-Based Approach to Household Load Curve Clustering and Prediction. IEEE Trans. Smart Grid
**2018**, 9, 5196–5206. [Google Scholar] [CrossRef] - Grunewald, P.; Diakonova, M. The Electricity Footprint of Household Activities - Implications for Demand Models. Energy Build.
**2018**, 174, 635–641. [Google Scholar] [CrossRef] - Carvallo, J.P.; Larsen, P.H.; Sanstad, A.H.; Goldman, C.A. Long Term Load Forecasting Accuracy in Electric Utility Integrated Resource Planning. Energy Policy
**2018**, 119, 410–422. [Google Scholar] [CrossRef][Green Version] - Bouveyron, C.; Bozzi, L.; Jacques, J.; Jollois, F. The Functional Latent Block Model for the Co-clustering of Electricity Consumption Curves. J. R. Stat. Soc. Ser. C (Appl. Stat.)
**2018**, 67, 897–915. [Google Scholar] [CrossRef][Green Version] - Oprea, S.-V.; Pîrjan, A.; Căruțașu, G.; Petroșanu, D.-M.; Bâra, A.; Stănică, J.-L.; Coculescu, C. Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data. Sensors
**2018**, 18, 1443. [Google Scholar] [CrossRef][Green Version] - Olaniyan, K.; McLellan, B.; Ogata, S.; Tezuka, T. Estimating Residential Electricity Consumption in Nigeria to Support Energy Transitions. Sustainability
**2018**, 10, 1440. [Google Scholar] [CrossRef][Green Version] - Fabisz, K.; Filipowska, A.; Hossa, T.; Hofman, R. Profiling of Prosumers for the Needs of Energy Demand Estimation in Microgrids. In Proceedings of the 2014 5th International Renewable Energy Congress (IREC), Hammamet, Tunisia, 25–27 March 2014; 2014; pp. 1–6. [Google Scholar]
- Doğan, R.; Akarslan, E. Investigation of Electrical Characteristics of Residential Light Bulbs in Load Modelling Studies with Novel Similarity Score Method. IET Gener. Transm. Amp. Distrib.
**2020**, 14, 5364–5371. [Google Scholar] [CrossRef] - Haq, E.U.; Lyu, X.; Jia, Y.; Hua, M.; Ahmad, F. Forecasting Household Electric Appliances Consumption and Peak Demand Based on Hybrid Machine Learning Approach. Energy Rep.
**2020**, 6, 1099–1105. [Google Scholar] [CrossRef] - Zhou, D.; Ma, S.; Hao, J.; Han, D.; Huang, D.; Yan, S.; Li, T. An Electricity Load Forecasting Model for Integrated Energy System Based on BiGAN and Transfer Learning. Energy Rep.
**2020**, 6, 3446–3461. [Google Scholar] [CrossRef] - Ertuğrul, Ö.F.; Tekin, H.; Tekin, R. A Novel Regression Method in Forecasting Short-Term Grid Electricity Load in Buildings That Were Connected to the Smart Grid. Electr Eng
**2021**, 103, 717–728. [Google Scholar] [CrossRef] - Hyeon, J.; Lee, H.; Ko, B.; Choi, H. Deep Learning-based Household Electric Energy Consumption Forecasting. J. Eng.
**2020**, 2020, 639–642. [Google Scholar] [CrossRef] - Kiprijanovska, I.; Stankoski, S.; Ilievski, I.; Jovanovski, S.; Gams, M.; Gjoreski, H. HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning. Energies
**2020**, 13, 2672. [Google Scholar] [CrossRef] - Shibano, K.; Mogi, G. Electricity Consumption Forecast Model Using Household Income: Case Study in Tanzania. Energies
**2020**, 13, 2497. [Google Scholar] [CrossRef] - Le, T.; Vo, M.T.; Kieu, T.; Hwang, E.; Rho, S.; Baik, S.W. Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building. Sensors
**2020**, 20, 2668. [Google Scholar] [CrossRef] - Son, H.; Kim, C. A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity. Sustainability
**2020**, 12, 3103. [Google Scholar] [CrossRef][Green Version] - Jenkins, D.P. Modelling Community Electricity Demand for UK and India. Sustain. Cities Soc.
**2020**, 55, 102054. [Google Scholar] [CrossRef] - Jasiński, T. Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach). Energies
**2020**, 13, 1263. [Google Scholar] [CrossRef][Green Version] - Zor, K.; Çelik, Ö.; Timur, O.; Teke, A. Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks. Energies
**2020**, 13, 1102. [Google Scholar] [CrossRef][Green Version] - Abera, F.Z. Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data. Wirel. Pers. Commun.
**2020**, 11, 65–82. [Google Scholar] [CrossRef] - Roy, S.S. Forecasting Heating and Cooling Loads of Buildings: A Comparative Performance Analysis. J. Ambient. Intell. Humaniz. Comput.
**2020**, 11, 1253–1264. [Google Scholar] [CrossRef] - Khammayom, N.; Maruyama, N.; Chaichana, C. Simplified Model of Cooling/Heating Load Prediction for Various Air-Conditioned Room Types. Energy Rep.
**2020**, 6, 344–351. [Google Scholar] [CrossRef] - Peña-Guzmán, C.; Rey, J. Forecasting Residential Electric Power Consumption for Bogotá Colombia Using Regression Models. Energy Rep.
**2020**, 6, 561–566. [Google Scholar] [CrossRef] - Li, C.; Tang, M.; Zhang, G.; Wang, R.; Tian, C. A Hybrid Short-Term Building Electrical Load Forecasting Model Combining the Periodic Pattern, Fuzzy System, and Wavelet Transform. Int. J. Fuzzy Syst.
**2020**, 22, 156–171. [Google Scholar] [CrossRef] - Liao, S.; Wei, L.; Kim, T.; Su, W. Modeling and Analysis of Residential Electricity Consumption Statistics: A Tracy-Widom Mixture Density Approximation. IEEE Access
**2020**, 8, 163558–163567. [Google Scholar] [CrossRef] - Cabello Eras, J.J.; Sousa Santos, V.; Sagastume Gutiérrez, A.; Guerra Plasencia, M.Á.; Haeseldonckx, D.; Vandecasteele, C. Tools to Improve Forecasting and Control of the Electricity Consumption in Hotels. J. Clean. Prod.
**2016**, 137, 803–812. [Google Scholar] [CrossRef] - Shine, P.; Scully, T.; Upton, J.; Murphy, M.D. Multiple Linear Regression Modelling of On-Farm Direct Water and Electricity Consumption on Pasture Based Dairy Farms. Comput. Electron. Agric.
**2018**, 148, 337–346. [Google Scholar] [CrossRef][Green Version] - Chen, Y.; Tan, H. Short-Term Prediction of Electric Demand in Building Sector via Hybrid Support Vector Regression. Appl. Energy
**2017**, 204, 1363–1374. [Google Scholar] [CrossRef] - To, W.-M.; Lee, P.K.C.; Lai, T.-M.; To, W.-M.; Lee, P.K.C.; Lai, T.-M. Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong. Energies
**2017**, 10, 885. [Google Scholar] [CrossRef][Green Version] - Le Cam, M.; Zmeureanu, R.; Daoud, A. Cascade-Based Short-Term Forecasting Method of the Electric Demand of HVAC System. Energy
**2017**, 119, 1098–1107. [Google Scholar] [CrossRef] - Rojas-Renteria, J.L.; Espinoza-Huerta, T.D.; Tovar-Pacheco, F.S.; Gonzalez-Perez, J.L.; Lozano-Dorantes, R. An Electrical Energy Consumption Monitoring and Forecasting System. Technol. Appl. Sci. Res.
**2016**, 6, 1130–1132. [Google Scholar] [CrossRef] - Christiansen, N.; Kaltschmitt, M.; Dzukowski, F.; Isensee, F. Electricity Consumption of Medical Plug Loads in Hospital Laboratories: Identification, Evaluation, Prediction and Verification. Energy Build.
**2015**, 107, 392–406. [Google Scholar] [CrossRef] - Tetlow, R.M.; van Dronkelaar, C.; Beaman, C.P.; Elmualim, A.A.; Couling, K. Identifying Behavioural Predictors of Small Power Electricity Consumption in Office Buildings. Build. Environ.
**2015**, 92, 75–85. [Google Scholar] [CrossRef] - Platon, R.; Dehkordi, V.R.; Martel, J. Hourly Prediction of a Building’s Electricity Consumption Using Case-Based Reasoning, Artificial Neural Networks and Principal Component Analysis. Energy Build.
**2015**, 92, 10–18. [Google Scholar] [CrossRef] - Nepal, B.; Yamaha, M.; Yokoe, A.; Yamaji, T. Electricity Load Forecasting Using Clustering and ARIMA Model for Energy Management in Buildings. Jpn. Archit. Rev.
**2020**, 3, 62–76. [Google Scholar] [CrossRef][Green Version] - Eras, J.C.; Santos, V.S.; Gutierrez, A.S.; Vandecasteele, C. Data Supporting the Improvement of Forecasting and Control of Electricity Consumption in Hotels. Data Brief
**2019**, 25, 104147. [Google Scholar] [CrossRef] - Cheng, C.-C.; Lee, D. Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results. Sensors
**2019**, 19, 3440. [Google Scholar] [CrossRef][Green Version] - Lim, C.; Park, B.; Lee, J.; Kim, E.S.; Shin, S. Electric Power Consumption Predictive Modeling of an Electric Propulsion Ship Considering the Marine Environment. Int. J. Nav. Archit. Ocean. Eng.
**2019**, 11, 765–781. [Google Scholar] [CrossRef] - Divina, F.; García Torres, M.; Goméz Vela, F.A.; Vázquez Noguera, J.L. A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings. Energies
**2019**, 12, 1934. [Google Scholar] [CrossRef][Green Version] - McNeil, M.A.; Karali, N.; Letschert, V. Forecasting Indonesia’s Electricity Load through 2030 and Peak Demand Reductions from Appliance and Lighting Efficiency. Energy Sustain. Dev.
**2019**, 49, 65–77. [Google Scholar] [CrossRef] - Yang, B.; Liu, F.; Zhang, M. A Loading Control Strategy for Electric Load Simulator Based on New Mapping Approach and Fuzzy Inference in Cerebellar Model Articulation Controller. Meas. Control.
**2019**, 52, 131–144. [Google Scholar] [CrossRef][Green Version] - Hwang, J.; Suh, D.; Otto, M.-O. Forecasting Electricity Consumption in Commercial Buildings Using a Machine Learning Approach. Energies
**2020**, 13, 2885. [Google Scholar] [CrossRef] - Müller, M.R. Electrical Load Forecasting in Disaggregated Levels Using Fuzzy ARTMAP Artificial Neural Network and Noise Removal by Singular Spectrum Analysis. SN Appl. Sci.
**2020**, 2, 1–10. [Google Scholar] [CrossRef] - Pamuła, T.; Pamuła, W. Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning. Energies
**2020**, 13, 2340. [Google Scholar] [CrossRef] - Yu, Z.; Bai, Y.; Fu, Q.; Chen, Y.; Mao, B. An Estimation Model on Electricity Consumption of New Metro Stations. J. Adv. Transp.
**2020**, 2020, 3423659. [Google Scholar] [CrossRef][Green Version] - Berk, K.; Hoffmann, A.; Müller, A. Probabilistic Forecasting of Industrial Electricity Load with Regime Switching Behavior. Int. J. Forecast.
**2018**, 34, 147–162. [Google Scholar] [CrossRef] - Valenzuela Guzman, M.; Valenzuela, M.A. Integrated Mechanical–Electrical Modeling of an AC Electric Mining Shovel and Evaluation of Power Requirements During a Truck Loading Cycle. IEEE Trans. Ind. Appl.
**2015**, 51, 2590–2599. [Google Scholar] [CrossRef] - Özşahin, Ş.; Singer, H. Development of an Artificial Neural Network Model to Minimize Power Consumption in the Milling of Heat-Treated and Untreated Wood. Kast. Üniversitesi Orman Fakültesi Derg.
**2019**, 19, 317–328. [Google Scholar] [CrossRef] - Elduque, A.; Elduque, D.; Pina, C.; Clavería, I.; Javierre, C. Electricity Consumption Estimation of the Polymer Material Injection-Molding Manufacturing Process: Empirical Model and Application. Materials
**2018**, 11, 1740. [Google Scholar] [CrossRef][Green Version] - Wu, D.-C.; Amini, A.; Razban, A.; Chen, J. ARC Algorithm: A Novel Approach to Forecast and Manage Daily Electrical Maximum Demand. Energy
**2018**, 154, 383–389. [Google Scholar] [CrossRef][Green Version] - Zhang, L.; Shi, J.; Wang, L.; Xu, C. Electricity, Heat, and Gas Load Forecasting Based on Deep Multitask Learning in Industrial-Park Integrated Energy System. Entropy
**2020**, 22, 1355. [Google Scholar] [CrossRef] - Devaru, D.G. Regression Model to Estimate the Electrical Energy Consumption of Lumber Sawing Based on the Product, Process, and System Parameters. Energy Effic.
**2020**, 13, 1799–1824. [Google Scholar] [CrossRef] - Son, N.; Yang, S.; Na, J. Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting. Appl. Sci.
**2020**, 10, 6489. [Google Scholar] [CrossRef] - Carlsson, L.S.; Samuelsson, P.B.; Jönsson, P.G. Modeling the Effect of Scrap on the Electrical Energy Consumption of an Electric Arc Furnace. Steel Res. Int.
**2020**, 91, 2000053. [Google Scholar] [CrossRef] - Goswami, K.; Samuel, G.L. Non-Linear Model of Energy Consumption for in-Process Control in Electrical Discharge Machining. Int. J. Adv. Manuf. Technol.
**2020**, 110, 1543–1561. [Google Scholar] [CrossRef] - Carlsson, L.S.; Samuelsson, P.B.; Jönsson, P.G. Interpretable Machine Learning—Tools to Interpret the Predictions of a Machine Learning Model Predicting the Electrical Energy Consumption of an Electric Arc Furnace. Steel Res. Int.
**2020**, 91, 2000053. [Google Scholar] [CrossRef] - Zhu, L.; Li, M.S.; Wu, Q.H.; Jiang, L. Short-Term Natural Gas Demand Prediction Based on Support Vector Regression with False Neighbours Filtered. Energy
**2015**, 80, 428–436. [Google Scholar] [CrossRef] - Wu, L.; Liu, S.; Chen, H.; Zhang, N. Using a Novel Grey System Model to Forecast Natural Gas Consumption in China. Math. Probl. Eng.
**2015**, 2015, 686501. [Google Scholar] [CrossRef] - Su, H.; Zio, E.; Zhang, J.; Xu, M.; Li, X.; Zhang, Z. A Hybrid Hourly Natural Gas Demand Forecasting Method Based on the Integration of Wavelet Transform and Enhanced Deep-RNN Model. Energy
**2019**, 178, 585–597. [Google Scholar] [CrossRef][Green Version] - Laib, O.; Khadir, M.T.; Mihaylova, L. Toward Efficient Energy Syst. Based on Natural Gas Consumption Prediction with LSTM Recurrent Neural Networks. Energy
**2019**, 177, 530–542. [Google Scholar] [CrossRef] - Hauser, P.; Heidari, S.; Weber, C.; Möst, D. Does Increasing Natural Gas Demand in the Power Sector Pose a Threat of Congestion to the German Gas Grid? A Model-Coupling Approach. Energies
**2019**, 12, 2159. [Google Scholar] [CrossRef][Green Version] - Khani, H.; Farag, H.E.Z. An Online-Calibrated Time Series Based Model for Day-Ahead Natural Gas Demand Forecasting. IEEE Trans. Ind. Inform.
**2019**, 15, 2112–2123. [Google Scholar] [CrossRef] - Mu, X.-Z.; Li, G.-H.; Hu, G.-W. Modeling and Scenario Prediction of a Natural Gas Demand System Based on a System Dynamics Method. Pet. Sci.
**2018**, 15, 912–924. [Google Scholar] [CrossRef][Green Version] - Merkel, G.; Povinelli, R.; Brown, R. Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression. Energies
**2018**, 11, 2008. [Google Scholar] [CrossRef][Green Version] - Anagnostis, A.; Papageorgiou, E.; Bochtis, D. Application of Artificial Neural Networks for Natural Gas Consumption Forecasting. Sustainability
**2020**, 12, 6409. [Google Scholar] [CrossRef] - Papageorgiou, K.; Papageorgiou, E.I.; Poczeta, K.; Bochtis, D.; Stamoulis, G. Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System. Energies
**2020**, 13, 2317. [Google Scholar] [CrossRef] - Zheng, C.; Wu, W.-Z.; Jiang, J.; Li, Q. Forecasting Natural Gas Consumption of China Using a Novel Grey Model. Complexity
**2020**, 2020, 3257328. [Google Scholar] [CrossRef] - Erdem, O.E.; Kesen, S.E. Estimation of Turkey’s Natural Gas Consumption by Machine Learning Techniques. Gazi Univ. J. Sci.
**2020**, 33, 120–133. [Google Scholar] [CrossRef] - Fagiani, M.; Squartini, S.; Gabrielli, L.; Spinsante, S.; Piazza, F. Domestic Water and Natural Gas Demand Forecasting by Using Heterogeneous Data: A Preliminary Study. In Advances in Neural Networks: Computational and Theoretical Issues; Bassis, S., Esposito, A., Morabito, F.C., Eds.; Springer International Publishing: Cham, Switzerland, 2015; Volume 37, pp. 185–194. ISBN 978-3-319-18163-9. [Google Scholar]
- Gascón, A.; Sánchez-Úbeda, E.F. Automatic Specification of Piecewise Linear Additive Models: Application to Forecasting Natural Gas Demand. Stat. Comput.
**2018**, 28, 201–217. [Google Scholar] [CrossRef] - Scarpa, F.; Bianco, V.; Scarpa, F.; Bianco, V. Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector. Energies
**2017**, 10, 1879. [Google Scholar] [CrossRef][Green Version] - Akpinar, M.; YumuşAk, N. Naive Forecasting of Household Natural Gas Consumption with Sliding Window Approach. Turk. J. Electr. Eng. Comput. Sci.
**2017**, 25, 30–45. [Google Scholar] [CrossRef] - Vidoza, J.A.; Gallo, W.L.R. Projection of Fossil Fuels Consumption in the Venezuelan Electricity Generation Industry. Energy
**2016**, 104, 237–249. [Google Scholar] [CrossRef] - Hribar, R.; Potočnik, P.; Šilc, J.; Papa, G. A Comparison of Models for Forecasting the Residential Natural Gas Demand of an Urban Area. Energy
**2019**, 167, 511–522. [Google Scholar] [CrossRef] - Zhou, Z.; Qin, Q.; Dong, X. Ecological Study on Understanding and Predicting China’s Natural Gas Consumption. Ekoloji
**2019**, 28, 4581–4587. [Google Scholar] - Daei Jafari, F.; Sadigh, R. Modeling and Forecasting Residential Natural Gas Demand in IRAN. Rev. Gestão Tecnol.
**2019**, 19, 33–57. [Google Scholar] [CrossRef] - Bezyan, B.; Zmeureanu, R. Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada. Energies
**2020**, 13, 1158. [Google Scholar] [CrossRef][Green Version] - Kovačič, M.; Dolenc, F. Prediction of the Natural Gas Consumption in Chemical Processing Facilities with Genetic Programming. Genet. Program. Evolvable Mach.
**2016**, 17, 231–249. [Google Scholar] [CrossRef] - Afshari, A.; Liu, N. Inverse Modeling of the Urban Energy System Using Hourly Electricity Demand and Weather Measurements, Part 2: Gray-Box Model. Energy Build.
**2017**, 157, 139–156. [Google Scholar] [CrossRef] - Sajjadi, S.; Shamshirband, S.; Alizamir, M.; Yee, P.L.; Mansor, Z.; Manaf, A.A.; Altameem, T.A.; Mostafaeipour, A. Extreme Learning Machine for Prediction of Heat Load in District Heating Systems. Energy Build.
**2016**, 122, 222–227. [Google Scholar] [CrossRef] - Dalipi, F.; Yildirim Yayilgan, S.; Gebremedhin, A. Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study. Appl. Comput. Intell. Soft Comput.
**2016**, 2016, 3403150. [Google Scholar] [CrossRef][Green Version] - Ruhnau, O.; Hirth, L.; Praktiknjo, A. Time Series of Heat Demand and Heat Pump Efficiency for Energy System Modeling. Sci. Data
**2019**, 6, 189. [Google Scholar] [CrossRef] [PubMed] - Di Lascio, F.M.L.; Menapace, A.; Righetti, M. Joint and Conditional Dependence Modelling of Peak District Heating Demand and Outdoor Temperature: A Copula-Based Approach. Stat. Methods Appl.
**2020**, 29, 373–395. [Google Scholar] [CrossRef] - Liu, J.; Wang, X.; Zhao, Y.; Dong, B.; Lu, K.; Wang, R. Heating Load Forecasting for Combined Heat and Power Plants Via Strand-Based LSTM. IEEE Access
**2020**, 8, 33360–33369. [Google Scholar] [CrossRef] - Turhan, C.; Kazanasmaz, T.; Gökçen Akkurt, G. Performance Indices of Soft Comput. Models to Predict the Heat Load of Buildings in Terms of Architectural Indicators. J. Therm. Eng.
**2017**, 3, 1358–1374. [Google Scholar] [CrossRef] - Sholahudin, S.; Han, H. Simplified Dynamic Neural Network Model to Predict Heating Load of a Building Using Taguchi Method. Energy
**2016**, 115, 1672–1678. [Google Scholar] [CrossRef] - O’Leary, T.; Belusko, M.; Whaley, D.; Bruno, F. Comparing the Energy Performance of Australian Houses Using NatHERS Modelling against Measured Household Energy Consumption for Heating and Cooling. Energy Build.
**2016**, 119, 173–182. [Google Scholar] [CrossRef] - Sholahudin, S.; Han, H. Heating Load Predictions Using The Static Neural Networks Method. Int. J. Technol.
**2015**, 6, 946. [Google Scholar] [CrossRef] - Attanasio, A.; Piscitelli, M.; Chiusano, S.; Capozzoli, A.; Cerquitelli, T. Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates. Energies
**2019**, 12, 1273. [Google Scholar] [CrossRef][Green Version] - Maljkovic, D. Modelling Influential Factors of Consumption in Buildings Connected to District Heating Systems. Energies
**2019**, 12, 586. [Google Scholar] [CrossRef][Green Version] - Eseye, A.T.; Lehtonen, M. Short-Term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models. IEEE Trans. Ind. Inf.
**2020**, 16, 7743–7755. [Google Scholar] [CrossRef] - Sajjad, M.; Khan, S.U.; Khan, N.; Haq, I.U.; Ullah, A.; Lee, M.Y.; Baik, S.W. Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model. Sensors
**2020**, 20, 6419. [Google Scholar] [CrossRef] - Oh, S.; Kim, C.; Heo, J.; Do, S.L.; Kim, K.H. Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data. Energies
**2020**, 13, 3189. [Google Scholar] [CrossRef] - Moradzadeh, A.; Mansour-Saatloo, A.; Mohammadi-Ivatloo, B.; Anvari-Moghaddam, A. Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings. Appl. Sci.
**2020**, 10, 3829. [Google Scholar] [CrossRef] - De Jaeger, I.; Vandermeulen, A.; van der Heijde, B.; Helsen, L.; Saelens, D. Aggregating Set-Point Temperature Profiles for Archetype-Based: Simulations of the Space Heat Demand within Residential Districts. J. Build. Perform. Simul.
**2020**, 13, 285–300. [Google Scholar] [CrossRef] - Panyafong, A.; Neamsorn, N.; Chaichana, C. Heat Load Estimation Using Artificial Neural Network. Energy Rep.
**2020**, 6, 742–747. [Google Scholar] [CrossRef] - Lim, H.S.; Kim, G. Development of Regression Models Considering Time-Lag and Aerosols for Predicting Heating Loads in Buildings. Adv. Civ. Eng.
**2018**, 2018, 4878021. [Google Scholar] [CrossRef][Green Version] - Ahmadzadehtalatapeh, M. Feasibility Study of a Water-to-Air Heat Pipe Based Heat Exchanger for Cooling Load Reduction and Energy Saving in the o Ce Buildings: A Simulation Study. Sci. Iran.
**2017**, 24, 1040–1050. [Google Scholar] [CrossRef][Green Version] - Kitsuya, T.; Zang, W.; Kumagai, S.; Kishima, S. Target for Heat Capacity Consumption That Considers Safety, Energy Savings, and Comfort: A Room Heat Capacity Model Using a Two-Phase Difference Integration Method. Int. J. Energy Environ. Eng.
**2017**, 8, 1–8. [Google Scholar] [CrossRef][Green Version] - Capozzoli, A.; Grassi, D.; Causone, F. Estimation Models of Heating Energy Consumption in Schools for Local Authorities Planning. Energy Build.
**2015**, 105, 302–313. [Google Scholar] [CrossRef][Green Version] - Chehade, A.; Louahlia-Gualous, H.; Le Masson, S.; Lépinasse, E. Experimental Investigations and Modeling of a Loop Thermosyphon for Cooling with Zero Electrical Consumption. Appl. Therm. Eng.
**2015**, 87, 559–573. [Google Scholar] [CrossRef] - Jovanović, R.Ž.; Sretenović, A.A.; Živković, B.D. Ensemble of Various Neural Networks for Prediction of Heating Energy Consumption. Energy Build.
**2015**, 94, 189–199. [Google Scholar] [CrossRef] - Yu, S.; Cui, Y.; Shao, Y.; Han, F. Simulation Research on the Effect of Coupled Heat and Moisture Transfer on the Energy Consumption and Indoor Environment of Public Buildings. Energies
**2019**, 12, 141. [Google Scholar] [CrossRef][Green Version] - Chaichana, C.; Thiangchanta, S. The Heat Load Modelling for an Air-Conditioned Room Using Buckingham-Pi Theorem. Energy Rep.
**2020**, 6, 656–661. [Google Scholar] [CrossRef] - Thiangchanta, S.; Chaichana, C. The Multiple Linear Regression Models of Heat Load for Air-Conditioned Room. Energy Rep.
**2020**, 6, 972–977. [Google Scholar] [CrossRef] - Guo, J.; Yang, H. A Fault Detection Method for Heat Loss in a Tyre Vulcanization Workshop Using a Dynamic Energy Consumption Model and Predictive Baselines. Appl. Therm. Eng.
**2015**, 90, 711–721. [Google Scholar] [CrossRef] - Jovanović, R.; Sretenović, A. Ensemble of Radial Basis Neural Networks with K-Means Clustering for Heating Energy Consumption Prediction. FME Trans.
**2017**, 45, 51–57. [Google Scholar] [CrossRef][Green Version] - Vergara, G.; Alonso-Barba, J.I.; Soria-Olivas, E.; Gámez, J.A.; Domínguez, M. Random Extreme Learning Machines to Predict Electric Load in Buildings. Prog. Artif. Intell.
**2016**, 5, 129–135. [Google Scholar] [CrossRef] - Yang, Z.-C. Electric Load Movement Forecasting Based on the DFT Interpolation with Periodic Extension. J. Circuits Syst. Comput.
**2015**, 24, 1550123. [Google Scholar] [CrossRef] - Si, P.; Li, A.; Rong, X.; Feng, Y.; Yang, Z.; Gao, Q. New Optimized Model for Water Temperature Calculation of River-Water Source Heat Pump and Its Application in Simulation of Energy Consumption. Renew. Energy
**2015**, 84, 65–73. [Google Scholar] [CrossRef] - Seo, D.; Koo, C.; Hong, T. A Lagrangian Finite Element Model for Estimating the Heating and Cooling Demand of a Residential Building with a Different Envelope Design. Appl. Energy
**2015**, 142, 66–79. [Google Scholar] [CrossRef] - Kosasih, E.A.; Ruhyat, N. Combination of Electric Air Heater and Refrigeration System to Reduce Energy Consumption: A Simulation of Thermodynamic System. Int. J. Technol.
**2016**, 7, 288. [Google Scholar] [CrossRef][Green Version] - Zhang, L.; Zhang, Y. Research on Heat Recovery Technology for Reducing the Energy Consumption of Dedicated Ventilation Systems: An Application to the Operating Model of a Laboratory. Energies
**2016**, 9, 24. [Google Scholar] [CrossRef][Green Version] - Szoplik, J. Forecasting of Natural Gas Consumption with Artificial Neural Networks. Energy
**2015**, 85, 208–220. [Google Scholar] [CrossRef] - Popkov, Y.S.; Popkov, A.Y.; Dubnov, Y.A. Elements of Randomized Forecasting and Its Application to Daily Electrical Load Prediction in a Regional Power System. Autom. Remote. Control.
**2020**, 81, 1286–1306. [Google Scholar] [CrossRef] - Vink, K.; Ankyu, E.; Kikuchi, Y. Long-Term Forecasting Potential of Photo-Voltaic Electricity Generation and Demand Using R. Appl. Sci.
**2020**, 10, 4462. [Google Scholar] [CrossRef]

**Figure 2.**Sectors and energy carriers. The number of published articles is shown by sector and energy carrier. In most articles, the energy consumption of all sectors is modeled, e.g., of an entire region. Electricity consumption is modeled the most. In the residential and commercial sectors, a significant number of articles focus on heating and cooling demand in buildings.

**Figure 3.**The relative share of energy carriers within each category of techniques. The relative share of appearances of each energy carrier within each of the five major categories of techniques (see Section 3) is shown along with the total number of appearances (n) of each category. Approaches like ML and statistical techniques are used the most and are largely applied to model electricity consumption. These methods mainly rely on historic consumption data, which is particularly well available for electricity consumption. Engineering-based approaches are used less frequently overall but are suited to model heat/cooling demand, especially in the context of building simulation.

**Figure 4.**ML techniques. The number of appearances of each technique within the cluster of ML techniques is displayed. Supervised learning with ANN is the predominant ML technique. Clustering algorithms are frequently used for data preparation and feature selection. Decision trees and Bayesian networks are used rather rarely.

**Figure 5.**Combination of techniques. An arc connects two categories whenever in an article a combination of techniques from the two categories was used. Self-arcs indicate that a technique was used as a stand-alone approach or was combined with a technique from the same category. The size of the self-arc/arc at its start and endpoint represents the share of stand-alone/combined techniques relative to the total number of articles.

**Figure 6.**Input data—frequency of usage. The number of articles relying on the seven different types of input data is shown.

**Figure 7.**Input data type by the method. For each method, the relative share of the seven input data types is shown. Across all methods, engineering-based approaches rely less on historic energy demands.

**Figure 8.**The relative share of categories of techniques by the temporal horizon and spatio-temporal resolution. The left figure shows that ML and metaheuristic techniques are predominantly employed for short-term projections while engineering-based approaches are used for longer timeframes. The middle figure shows that there is an overall tendency towards hourly time steps. Techniques are used equally across all temporal resolutions, except ML, which decrease for longer time-steps. The right figure shows that buildings, households, and regions are analyzed the most. Engineering-based models stand out in showing a clear tendency towards a high level of detail.

**Figure 9.**Histogram of MAPE values in analyzed articles. Multiple values per article are possible. skew = 3.99, kurtosis = 23.07.

**Figure 10.**Boxplot of MAPE values by categories of techniques. Box represents the interquartile range (IQR). Whiskers show a range of data beyond the 1st and 3rd quartile and extend until 1.5*IQR on each side, ending at maximum and minimum data points within that interval. Outliers are not shown. The green line represents the median. The red diamond represents mean. Among the analyzed articles, accuracy measured by MAPE does not seem to depend necessarily on the chosen technique.

**Figure 11.**Boxplot of MAPE values by spatial levels of detail. The same mode of display as in Figure 10. This shows that a higher level of detail results in lower accuracy. This is because aggregated loads on the country and regional level are smoother and have stronger temporal patterns compared to loads of individual appliances or households, which are subject to behavioral patterns and a higher degree of randomness.

**Table 1.**Overview of recent systematic literature by content. In each line, black squares (■) indicate topics covered in the given review. Most reviews cover several sectors or energy carriers and analyze model inputs and spatio-temporal features. Few reviews analyze model accuracies and only the present article covers all the aspects.

Techniques | Energy Carriers | Sectors | Spatio-Temporal Features | Input Data | Accuracy | Articles | Reference | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Electricity | Thermal | Natural gas | Primary energy | Residential | Commercial | Industries | All sectors together | Temp. horizon | Temp. resolution | Spatial resolution | |||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | 41 | [9] | |||||||

■ | ■ | ■ | ■ | ■ | n/a | [11] | |||||||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | 63 | [10] | ||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | 483 | [4] | |||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | 130 | [12] | ||||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | 39 | [13] | |||||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | 116 | [14] | ||||

■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | ■ | 419 | This article |

**Table 2.**Assessment criteria. Overview of analysis criteria defining the collected data during step three of the review procedure. Each item represents a property characterizing the techniques applied in the respective articles. A short description and possible values are given. For mutually exclusive criteria only one value is possible, while for non-exclusive properties multiple values can be given and counted multiple times.

Analysis Criteria | Description | Possible Values | Mutually Exclusive |
---|---|---|---|

Technique | Modeling technique applied | Artificial neural network, support vector machine, regression, autoregressive methods, etc. | No |

Category of techniques | General category of applied technique | Statistical, machine learning, metaheuristic, stochastic/fuzzy/grey, and engineering-based techniques | No |

Technique combination | A single technique or a combination of techniques was applied | Stand-alone or hybrid approach | Yes |

Model inputs | Inputs for energy demand models serving as explanatory variables and predictors | Data describing historic load, calendar information, weather, economy, demographics, environment, prices, behavior, and information about the technical system | No |

Energy carrier | Forecasted/modeled type of energy | Electricity, natural gas, energy for heating and cooling | No |

Sector | Economic sector or consumer group which is modeled | Industrial, commercial, residential, all sectors | No |

Technical system | Applications or technical systems, which are modeled | Power grid, gas grid, district heating, building, production | No |

Spatial resolution | Spatial level of detail of models | Country, regions (e.g., district), households/buildings, appliances | Yes |

Temporal resolution | Scale of time steps that are described by the models | Sub-hourly, hourly, daily, above daily | Yes |

Temporal horizon | Timespan that is covered by the models | Short-term (up to one day), medium-term (several weeks or months), long-term (one year and above) | Yes |

Accuracy | Performance evaluation of presented models | Numeric values for MAPE | No |

**Table 3.**Model inputs. Classification of possible data-sets used as model input along with examples of data-sets.

Model Inputs | Examples |
---|---|

Historic energy demand | Historic load, electricity, heating, cooling, or natural gas demand |

Weather data | Outside temperature, atmospheric pressure, cooling and heating degree days, humidity, solar radiation, wind speed |

Calendar data | Time of day, day of the week, month, holidays, bridge days, seasons, workday, working hours, operating time of appliance |

Demographic or economic data | Economic indicators: gross domestic product (GDP), gross national income (GNI), level of production, income, import and export level of a region; demographic indicators: human development indices, population, number of dwellers/buildings/residences, age, sex, education, infant mortality |

Technical system data | Appliance data: equipment installed, number of appliances, efficiency, material properties, air change ratio, flow rate, outlet/inlet temperatures, rated power of the equipment, impedance Building data: floor space, number of bedrooms, transmission factor, building type, age of the building, efficiency rating, geometry of the building, the status of refurbishment, window area, building material, indoor temperature, indoor humidity |

Usage and behavioral data | Time-use survey data, building usage (main residency, rented, owned, etc.), occupancy/activity patterns, operation/usage time of a device |

Energy prices | Electricity and gas prices, tariffs, payment methods |

**Table 4.**Techniques and input data used (1/5). Compiled results of the analysis on techniques and input data. Each cell provides a short assessment of the respective data-technique combination. The corresponding articles for each cell are documented in detail in the structured reference lists in Table A4, Table A5, Table A6 and Table A7 in Appendix A.

Technique | Advantages and Disadvantages of Techniques | Historic Energy Demand | Weather Data | Calendar Data | Demographic or Economic Data | Technical System Data | Usage or Behavioral Data | Energy Prices |
---|---|---|---|---|---|---|---|---|

ANN | - +
- Established for classification and regression problems (high performance), low effort, handle non-linear relations, big variety of pre-set models, no knowledge of technical system needed
- −
- Risk of over-fitting, difficult interpretation (black-box), feature engineering required
| Contributions: 107 Impact: High; can be used as a single input Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous intensive use | Contributions: 65 Impact: High; predictor for heating and cooling and lighting systems Drawbacks: Limited explanatory value for other applications than heating/cooling/ lighting Outlook: Continuous intensive use | Contributions: 44 Impact: High; Predictor for regular daily, weekly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous intensive use | Contributions: 13 Impact: Low for short-term load prediction; high for long-term regional, sectoral, or national demand prediction Drawbacks: Low level of detail regarding individual consumer patterns, usually yearly or quarterly resolution Outlook: Continuous use in cases of long-term national or sectoral demand modeling | Contributions: 18 Impact: High; explanatory value regarding process internal and end-user devices Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Occasional use, could see intensification | Contributions: 6 Impact: Potentially high; explanatory value regarding individual consumer patterns Drawbacks: High effort to collect because the result of time-of- use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Rare use, could see intensification | Contributions: 4 Impact: Low Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Rare use |

Instance-based | - +
- Established for classification problems, good performance with a high number of features, for kernel machines there are many pre-set kernel functions to choose from, transforms nonlinear relations into linear ones in the feature space, robust against overfitting
- −
- Medium/high effort, dependent on the right choice of kernel, memory-intensive and limited scalability for big datasets
| Contributions: 35 Impact: High; usually complemented by additional features Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous intensive use | Contributions: 19 Impact: High; predictor for heating and cooling and lighting systems Drawbacks: Limited explanatory value for other applications than heating/cooling/lighting Outlook: Continuous intensive use | Contributions: 15 Impact: High; Predictor for regular daily, weekly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous intensive use | Contributions: 11 Impact: High for classification of regions or consumer groups; low for short-term load prediction Drawbacks: Low level of detail regarding individual consumer patterns, usually yearly or quarterly resolution Outlook: Continuous use in cases of regional or sectoral modeling | Contributions: 8 Impact: High; explanatory value regarding process internal and end-user devices Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Occasional use, could see intensification | Contributions: 0 Impact: Potentially high explanatory value for classification of typical time steps considering individual consumer patterns Drawbacks: High effort to collect because the result of time-of- use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: No use, could see intensification | Contributions: 2 Impact: Low Drawbacks: rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Rare use |

Clustering | - +
- Finding natural groupings in an unsupervised learning process, easy to implement, different algorithms in place based on geographic distance (K-means), graph distance (affinity propagation), density (DBSCAN), or a hierarchical approach
- −
- Assumptions on the number and shape of clusters can be necessary and lead to mistakes
| Contributions: 34 Impact: High; used to find similar time steps or similar consumer groups Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous intensive use | Contributions: 13 Impact: High; used for finding similar days, a predictor for heating and cooling and lighting systems Drawbacks: Limited explanatory value for other applications than heating/cooling/ lighting Outlook: Continuous intensive use | Contributions: 12 Impact: High; for regular daily, weekly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous intensive use | Contributions: 9 Impact: High for classification of regions or consumer groups; low for short-term load prediction; Drawbacks: Low level of detail regarding individual consumer patterns, usually yearly or quarterly resolution Outlook: Continuous use in cases of regional or sectoral modeling | Contributions: 5 Impact: High; explanatory value regarding process internal and end-user devices Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Occasional use, could see intensification | Contributions: 2 Impact: Potentially high explanatory value for classification of typical time steps considering individual consumer patterns Drawbacks: High effort to collect because the result of time-of- use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Rare use, could see intensification | Contributions: 1 Impact: Low Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Rare use |

Technique | Advantages and Disadvantages of Techniques | Historic Energy Demand | Weather Data | Calendar Data | Demographic or Economic Data | Technical System Data | Usage or Behavioral Data | Energy Prices |
---|---|---|---|---|---|---|---|---|

Ensemble learning | - +
- Improved predictive performance by combi-ning the predictions of multiple models, in-creased robustness by reducing the variance of prediction errors
- −
- Requires additional knowledge to solve the bias-variance tradeoff
| Contributions: 19 Impact: High; always used Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous use | Contributions: 12 Impact: High; predictor for heating and cooling and lighting systems Drawbacks: Limited explanatory value for other applications than heating/cooling/ lighting Outlook: Continuous use | Contributions: 9 Impact: High; Predictor for regular daily, weekly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous use | Contributions: 1 Impact: Low for short-term load prediction; high for long-term regional, sectoral, or national demand prediction, Drawbacks: Low level of detail regarding individual consumer patterns, usually yearly or quarterly resolution Outlook: Use in cases of regional or sectoral modeling | Contributions: 3 Impact: Potentially high; explanatory value regarding process internal and end-user devices Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Occasional use, could see intensification | Contributions: 1 Impact: Potentially high; explanatory value regarding individual consumer patterns Drawbacks: a high effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Rare use, could see intensification | Contributions: 1 Impact: Low Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Rare use |

Deep learning | - +
- Established for classification and regression problems, can learn complex patterns be using hidden layers creating intermediary representations of the data, reduced need for feature engineering using drop-out layers
- −
- Large amounts of training data required, specialized algorithms, computationally intensive to train, require additional expertise to tune
| Contributions: 17 Impact: High; always used Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous use | Contributions: 9 Impact: High; often used for short term load forecasting Drawbacks: Limited explanatory value for other applications than heating/ cooling/lighting Outlook: Continuous use | Contributions: 7 Impact: High; Predictor for regular daily, weekly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous use | Contributions: 1 Impact: Low for short-term load prediction; high for long-term regional, sectoral, or national demand prediction Drawbacks: Low level of detail regarding individual consumer patterns, usually yearly or quarterly resolution Outlook: Use in cases of regional or sectoral modeling | Contributions: 1 Impact: Potentially high; explanatory value regarding process internal and end-user devices Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Rare use, could see intensification | Contributions: 1 Impact: Potentially high; explanatory value regarding individual consumer patterns Drawbacks: High effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Rare use, could see intensification | Contributions: 0 Impact: Low Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: No use |

Bayesian algorithms | - +
- Established for classification, basic models (Naïve Bayes) have low implementation effort, good performance, good scalability, are able to handle conflicting/limited information and nonlinear relations
- −
- Design of advanced models (B. networks) requires expert knowledge or good data to learn from, computationally expensive, therefore simplifications are used, however, they assume conditional independence between input features, which is rarely true
| Contributions: 9 Impact: High if used for forecasting Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous use | Contributions: 8 Impact: High; used in almost all cases, often used for forecasting for heating and cooling demand Drawbacks: Limited explanatory value for other applications than heating/ cooling/lighting Outlook: Continuous use | Contributions: 5 Impact: High; used in short-term forecasts, Predictor for regular daily, weekly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous use | Contributions: 2 Impact High for classification of regions or consumer groups; low for short-term load prediction; Drawbacks: Low level of detail regarding individual consumer patterns, usually yearly or quarterly resolution Outlook: Use in cases of regional or sectoral modeling | Contributions: 2 Impact: Low for forecasting, used in cases of algorithms for energy management and system control Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Sporadic use | Contributions: 1 Impact: Low for forecasting; potentially high for classification of typical time steps considering individual consumer patterns, used for simulations of demand in smart grids Drawbacks: High effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Rare use | Contributions: 1 Impact: Low, can be used for simulation of smart grids Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Rare use |

**Table 6.**Techniques and input data used (3/5). Continuation of Table 5.

Technique | Advantages and Disadvantages of Techniques | Historic Energy Demand | Weather Data | Calendar Data | Demographic or Economic Data | Technical System Data | Usage or Behavioral Data | Energy Prices |
---|---|---|---|---|---|---|---|---|

Decision trees | - +
- Established for classification, rarely used for regression, handles non-linear relationships by splitting data into homogenous sub-samples, low effort in data preparation, robust to outliers or missing values, good scalability
- −
- Risk of overfitting, very data sensitive, a small change in data can result in a major change of tree structure
| Contributions: 7 Impact: High; always used Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous use | Contributions: 5 Impact: High; used in almost all cases, often used for forecasting for heating and cooling demand Drawbacks: Limited explanatory value for other applications than heating/ cooling/lighting Outlook: Continuous use | Contributions: 5 Impact: High; used in short-term forecasts, a predictor for regular daily, week-ly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous use | Contributions: 3 Impact: High for classification of regions or consumer groups; low for short-term load prediction; Drawbacks: Low level of detail regarding individual consumer patterns, usually yearly or quarterly resolution Outlook: Continuous use in cases of regional or sectoral modeling | Contributions: 3 Impact: High; explanatory value regarding process internal and end-user devices Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Occasional use, could see intensification | Contributions: 0 Impact: Potentially high; explanatory value for classification of typical time steps considering individual consumer patterns Drawbacks: High effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations, Outlook: Could see intensification | Contributions: 0 Impact: Low Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Rare use |

Regression | - +
- Low implementation effort, good performance, computationally inexpensive, white box character, measures against overfitting in place (regularization)
- −
- Risk of underfitting, sensitive to outliers, the underlying assumption that features are independent does not hold in reality
| Contributions: 88 Impact: High; the dependent variable Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous intensive use | Contributions: 50 Impact: High; one of the most used independent variables, especially for heating/ cooling/lighting Drawbacks: Limited explanatory value for other applications than heating/cooling/lighting Outlook: Continuous intensive use | Contributions: 31 Impact: High; predictor for regular daily, weekly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous intensive use | Contributions: 31 Impact: High for long-term regional, sectoral or national demand prediction Drawbacks: Low level of detail regarding individual consumer properties, usually yearly or quarterly resolution Outlook: Continuous intensive use in cases of regional or sectoral modeling | Contributions: 13 Impact: High; explanatory value regarding process internal and end-user devices Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Occasional use, could see intensification | Contributions: 10 Impact: Potentially high; explanatory value regarding individual consumer patterns Drawbacks: High effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Occasional use, could see intensification | Contributions: 3 Impact: Low Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Rare use |

TSA/ARCH | - +
- Low implementation effort, data cleaning, and understanding is done at the same time, uncovers patterns in data like autocorrelation and seasonality, filters out noise, lots of pre-set models
- −
- Cannot predict unpreceded events, poor handling of outliers which can be propagated into future, not many techniques to deal with large numbers of variables and complex relationships, less suitable for long-term forecasting
| Contributions: 78 Impact: High; always used, often used as a single input Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous intensive use | Contributions: 20 Impact: Medium; used as an external variable, especially for heating/cooling/ lighting Drawbacks: Limited explanatory value for other applications than heating/cooling/lighting Outlook: Continuous use | Contributions: 19 Impact: Medium; used as an external variable, a predictor for regular temporal patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous use | Contributions: 10 Impact: Low for short-term load prediction; medium for long-term regional, sectoral, or national demand prediction Drawbacks: Low level of detail regarding individual consumer patterns, usually yearly or quarterly resolution Outlook: Use in cases of regional or sectoral modeling | Contributions: 0 Impact: Low; use of many external variables is generally rare with TSA Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Rare use | Contributions: 1 Impact: Low; use of many external variables is generally rare with TSA Drawbacks: High effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Rare use | Contributions: 1 Impact: Low; since external variables are generally rarely used with TSA in general Drawbacks: Rarely considered as a predictor because of low price elasticity, price swings only in liberalized markets, difficult to obtain future values Outlook: Rare use |

**Table 7.**Techniques and input data used (4/5). Continuation of Table 6.

Technique | Advantages and Disadvantages of Techniques | Historic Energy Demand | Weather Data | Calendar Data | Demographic or Economic Data | Technical System Data | Usage or Behavioral Data | Energy Prices |
---|---|---|---|---|---|---|---|---|

Stochastic | - +
- Capacity to handle uncertainty regarding the occurrence of events and produce variations of possible outcomes, the underlying assumptions about the randomness can be tested statistically, allowing to estimate not only the expected value but also the variations of the expected valuesPotentially high implementation effort can be computationally expensive, results can be difficult to communicate
| Contributions: 33 Impact: High; used to define probability distribution on historic values Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous intensive use | Contributions: 15 Impact: High; predictor for heating and cooling and lighting systems Drawbacks: Limited explanatory value for other applications than heating/cooling/lighting Outlook: Continuous intensive use | Contributions: 15 Impact: High; predictor for regular daily, weekly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous intensive use | Contributions: 10 Impact: High for long-term regional, sectoral or national demand prediction Drawbacks: Low level of detail regarding individual consumer properties, usually yearly or quarterly resolution Outlook: Continuous intensive use in cases of regional or sectoral modeling | Contributions: 8 Impact: High; explanatory value regarding process internal and end-user devices Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Continuous use, could see intensification | Contributions: 8 Impact: High; explanatory value regarding individual consumer patterns Drawbacks: High effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Continuous use, could see intensification | Contributions: 2 Impact: Low; can be used for simulation of smart grids Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Sporadic use |

Fuzzy | - +
- Adopts vagueness in human reasoning modeling the degree of occurrence of an event, can display a range of possibilities for inputs by applying membership functions (fuzzyfication), can handle incomplete data, after the model’s ruleset is applied defuzzyfication can be done following different principles (e.g., weighted average), computationally inexpensive,
- −
- Results can be perceived as inaccurate, communication of results can be difficult, depends on expert knowledge to be set up, extensive validation and testing
| Contributions: 32 Impact: High; exact usage also depends on the other part of the hybrid model (ML, TSA, etc.) Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous intensive use | Contributions: 11 Impact: High; predictor for heating and cooling and lighting systems Drawbacks: Limited explanatory value for other applications than heating/cooling/lighting Outlook: Continuous intensive use | Contributions: 10 Impact: Medium; exact usage depends on an-other part of the hybrid model, usually no fuzziness about calendar information Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous use | Contributions: 7 Impact: High for long-term regional, sectoral or national demand prediction Drawbacks: Low level of detail regarding individual consumer properties, usually yearly or quarterly resolution Outlook: Continuous intensive use in cases of regional or sectoral modeling | Contributions: 3 Impact: Medium; exact usage depends on another part of the hybrid model, high explanatory value regarding process internal and end-user devices Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Occasional use, could see intensification | Contributions: 2 Impact: Potentially high; explanatory value regarding individual consumer patterns Drawbacks: High effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict the output of simulations Outlook: Occasional use, could see intensification | Contributions: 1 Impact: Low; can be used for simulation of smart grids Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Sporadic use |

Metaheuristic | - +
- Provide alternative and promising algorithms to solve optimization problems and efficiently search the solution space, variety of metaheuristic algorithms in place which requires little implementation effort
- −
- No guarantee that the global maximum is attained, selection of algorithm can be difficult, depending on adequate parameter tuning
| Contributions: 26 Impact: High; exact usage also depends on the other part of the hybrid model (ML, regression, etc.) Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous intensive use | Contributions: 17 Impact: high, often used as a predictor for heating/cooling/lighting Drawbacks: Limited explanatory value for other applications than heating/cooling/lighting Outlook: Continuous intensive use | Contributions: 7 Impact: Medium; exact usage depends on an-other part of the hybrid model Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without knowledge Outlook: Continuous use | Contributions: 7 Impact: High for long-term regional, sectoral or national demand prediction Drawbacks: Low level of detail regarding individual consumer properties, usually yearly or quarterly resolution Outlook: Continuous intensive use in cases of regional or sectoral modeling | Contributions: 2 Impact: Potentially high; explanatory value regarding process internal and end-user devices, exact usage depends on another part of the hybrid model Drawbacks: Difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Occasional use, could see intensification | Contributions: 1 Impact: Potentially high; explanatory value regarding individual consumer patterns Drawbacks: High effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Rare use, could see intensification | Contributions: 3 Impact: Low Drawbacks: Rarely considered as a predictor because demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Occasional use |

**Table 8.**Techniques and input data used (5/5). Continuation of Table 7.

Technique | Advantages and Disadvantages of Techniques | Historic Energy Demand | Weather Data | Calendar Data | Demographic or Economic Data | Technical System Data | Usage or Behavioral Data | Energy Prices |
---|---|---|---|---|---|---|---|---|

Engineering-based | - +
- White box character, revealing detailed input-output relations, Able to simulate energy demand for explorative and normative scenarios including disruptions that have no historic record
- −
- Data and knowledge-intensive, prediction accuracy can be low due to simplifications regarding the system
| Contributions: 21 Impact: High; historic demand used for validation of model outputs Drawbacks: Outputs dependent on data quality and availability Outlook: Continuous intensive use | Contributions: 22 Impact: high, often used as a predictor for heating/cooling/lighting Drawbacks: Limited explanatory value for other applications than heating/cooling/lighting Outlook: Continuous intensive use | Contributions: 8 Impact: Medium; not needed to describe physical input-output relations, a predictor for regular daily, weekly or annual patterns Drawbacks: Risk of overestimation of periodic routines, cannot account for special events without know-ledge Outlook: Continuous use | Contributions: 16 Impact: High for long-term regional, sectoral or national demand prediction Drawbacks: Low level of detail regarding individual consumer properties, usually yearly or quarterly resolution Outlook: Continuous intensive use in cases of regional or sectoral modeling | Contributions: 37 Impact: High; most important information to describe physical input-output relations, Drawbacks: High amount of data needed, difficult to collect, have to be measured using sensors, might be subject to data privacy or company secrets Outlook: Continuous intensive use | Contributions: 12 Impact: Potentially high; explanatory value regarding individual consumer patterns Drawbacks: High effort to collect because the result of time-of-use surveys, subject to privacy or company secrets, difficult to predict, the output of simulations Outlook: Continuous use, could see intensification | Contributions: 2 Impact: Low Drawbacks: Rarely considered as a predictor, not needed for physical input-output relations, demand has low price elasticity, price swings only in liberalized markets, difficult to obtain future values to use in predictions Outlook: Rare use |

**Table 9.**Summary of advantages and disadvantages as well as countermeasures to compensate drawbacks.

Technique | Advantages | Disadvantages | Countermeasures |
---|---|---|---|

ML | High predictive performance; Relatively low implementation effort; Able to handle nonlinear relations; Many pre-set model configurations are available; Can be used without deeper knowledge of technical system | Black box character; Risk of overfitting; Course of dimensionality; Risk of getting stuck in shallow local minima | Regularization; Ensemble learning; Appropriate feature selection; Variation of input layers and neurons; Usage of metaheuristic optimization during the training stage |

Statistical | Low implementation effort for basic models; White box character, revealing relations between independent and dependent variables; Especially TSA can be used with relatively low data requirements | Limitations when independent variables are correlated; Difficulties predicting extreme events and outliers; Slight risk of overfitting | Pre-processing of data, e.g., by transformation and decomposition; Variable selection using PCA; Coefficient adjustments using regularization |

Stochastic/Fuzzy/Grey | Appropriately addresses uncertainty about inputs allowing to estimate expected outputs and output variations by using quantiles, intervals, or density functions as representations; Able to deal with incomplete/inaccurate data; Able to simulate energy demand based on stochastic processes, providing generated data as inputs for other models | Can be considered unsatisfying for decision-makers since model outputs are afflicted with probabilistic or fuzzy expressions; Long computing times for repeated simulation of stochastic processes | Variable elimination algorithms; Usage of evaluative labels on model outputs to make the uncertainty more understandable (e.g., uncertainty is high or low) |

Metaheuristic | Provide alternative and promising methods to solve optimization problems and efficiently search the solution space to find global optima; can be applied to different types of problems; a high number of easy to implement algorithms in place; Can be incorporated into other models; | Requires additional knowledge and effort to implement in existing models; not unrestrictedly reliable in finding the optimal solution Can have low convergence rates and be time-consuming | Usage of existing and proven model combinations |

Engineering-based | White-box character, revealing detailed input-output relations based on laws of physics; Able to simulate energy demand for explorative and normative scenarios including disruptions that have no historic record | Data and knowledge-intensive for a description of the technical system; prediction accuracy can be low due to simplifications regarding the system | Prioritization of datasets and choice of representative samples; Use of publicly available datasets for aggregated consumers |

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**MDPI and ACS Style**

Verwiebe, P.A.; Seim, S.; Burges, S.; Schulz, L.; Müller-Kirchenbauer, J. Modeling Energy Demand—A Systematic Literature Review. *Energies* **2021**, *14*, 7859.
https://doi.org/10.3390/en14237859

**AMA Style**

Verwiebe PA, Seim S, Burges S, Schulz L, Müller-Kirchenbauer J. Modeling Energy Demand—A Systematic Literature Review. *Energies*. 2021; 14(23):7859.
https://doi.org/10.3390/en14237859

**Chicago/Turabian Style**

Verwiebe, Paul Anton, Stephan Seim, Simon Burges, Lennart Schulz, and Joachim Müller-Kirchenbauer. 2021. "Modeling Energy Demand—A Systematic Literature Review" *Energies* 14, no. 23: 7859.
https://doi.org/10.3390/en14237859