# State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{7}

^{8}

^{9}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology of Survey

## 3. State of the Art of ML Models in Energy Systems

_{ti}represents the target value, x

_{pi}represents the predicted value, and n is the number of data points.

#### 3.1. ANN

_{2}emissions (million Tons). Based on Figure 4, the use of clean energy nearly doubled, which led to a reduction in CO

_{2}emissions from about 109 million tons to 38 million tons. Therefore the proposed case has an effective role compared with the base case.

#### 3.2. MLP

#### 3.3. ELM and Other Advanced ANNs

#### 3.4. SVM

#### 3.5. WNN

#### 3.6. ANFIS

#### 3.7. Decision Trees

#### 3.8. Deep Learning

#### 3.9. Ensemble Methods

#### 3.10. Hybrid ML Models

^{2}), which led to higher accuracy of predictions compared with other machine learning techniques. This claim is evident in Figure 29, which presents the average values of RMSE for the four developed techniques.

#### 3.11. Comparative Analysis of ML Models

## 4. Conclusions

## Author Contributions

## Conflicts of Interest

## Acronyms

AR | Autoregressive |

ARMA | Autoregressive Moving Average |

ANNs | Artificial Neural Networks |

ANFIS | Adaptive Neuro-Fuzzy Inference System |

BANN | Back Propagation Neural Network |

BP | Back Propagation |

CFS | Correlation based Feature Selection |

CGP | Cartesian Genetic Programming |

CRBM | Conditional Restricted Boltzmann Machines |

CRO | Coral Reefs Optimization |

CWNN | Convolutional-Wavelet Neural Networks |

DL | Deep Learning |

DOPH | Direct Optimum Parallel Hybrid |

DT | Decision trees |

EANN | Evolutionary Artificial neural networks |

EEMD | Ensemble Empirical Mode Decomposition |

ELM | Extreme Learning Machine |

EMD | Empirical Mode Decomposition |

FCRBM | Factored Conditional Restricted Boltzmann Machine |

FFNN | Feed Forward Neural Network |

FIS | Fuzzy inference system |

FOARBF | Fruit Fly Optimization Algorithm Radial Basis Function |

FOAGRNN | Fruit Fly Optimization Algorithm Generalized Regression Neural Networks |

FOASVR | Fruit Fly Optimization Algorithm Support Vector Regression |

GA | Genetic Algorithm |

GANN | Neural Networks by Genetic Algorithm |

GARCH | Generalized Autoregressive Conditional Heteroskedasticity |

GHG | Greenhouse Gas |

GFF | General Factorization Framework |

GP | Gaussian Processes |

GPR | Gaussian Processes Regression |

GRNN | Generalized Regression Neural Networks |

IDSS | Intelligent Decision Support System |

IoT | Internet of Things |

KELM | kernel-based extreme learning machine |

KFCM | Kernel Fuzzy C Means |

KNN | K-Nearest Neighbors |

LM | Levenberg-Marquardt |

MARS | Multivariate Adaptive Regression Splines |

ML | Machine Learning |

MLP | Multilayer Perceptron |

MR | Multi-Resolution |

MLR | Multiple linear regression |

MTL | Method of transmission lines |

MRWNN | Multi-Resolution Wavelet Neural Network |

NARX | Nonlinear Auto-Regressive with external input |

NDP | Neuro-Dynamic Programming |

NN | Neural Networks |

NWP | Numerical Weather Prediction |

OLR | Outgoing long-wave radiation |

PR | Persistence model |

PSO | Particle Swarm Optimization |

PV | Photovoltaic |

r | Correlation coefficient |

RBF | Radial Basis Function |

RBFNN | Radial Basis Function Neural Networks |

RF | Random Forests |

RMSE | Root Mean Squared Error |

RNN | Recurrent Neural Network |

SANN | Subsequent Artificial Neural Networks |

SAPSO | Self-Adaptive Particle Swarm Optimization |

SARIMA | Seasonal Autoregressive Integrated Moving Average |

SCADA | Supervisory Control and Data Acquisition |

SCG | Scaled Conjugate Gradient |

SE | Solar radiation |

SEAT | A car manufacturing plant in Spain |

SOC | State of Charge |

SOFM | Self-Organizing Feature Map |

SVM | Support Vector Machine |

SP | Coral Reefs Optimization algorithm with species |

SVR | Support vector regression |

WPRE | Wind Power Ramp Events |

WNN | Wavelet Neural Network |

## References

- Deshmukh, M.; Deshmukh, S. Modeling of hybrid renewable energy systems. Renew. Sustain. Energy Rev.
**2008**, 12, 235–249. [Google Scholar] [CrossRef] - Elgerd, O.I. Electric Energy Systems Theory: An introduction; McGraw-Hill: New York, NY, USA, 1982. [Google Scholar]
- Gomez-Exposito, A.; Conejo, A.J.; Canizares, C. Electric Energy Systems: Analysis and Operation; CRC Press: Boca Raton, FL, USA, 2018. [Google Scholar]
- Quaschning, V. Understanding Renewable Energy Systems; Routledge: Abingdon, UK, 2016. [Google Scholar]
- Isabella, O.; Smets, A.; Jäger, K.; Zeman, M.; van Swaaij, R. Solar Energy: The Physics and Engineering of Photovoltaic Conversion, Technologies and Systems; UIT Cambridge Limited: Cambridge, UK, 2016. [Google Scholar]
- Palensky, P.; Dietrich, D. Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inform.
**2011**, 7, 381–388. [Google Scholar] [CrossRef] - Torabi, M.; Hashemi, S.; Saybani, M.R.; Shamshirband, S.; Mosav, A. A Hybrid clustering and classification technique for forecasting short-term energy consumption. Environ. Prog. Sustain. Energy
**2018**, 38, 66–76. [Google Scholar] [CrossRef] - Najafi, B.; Faizollahzadeh Ardabili, S.; Mosavi, A.; Shamshirband, S.; Rabczuk, T. An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and Energy Analysis. Energies
**2018**, 11, 860. [Google Scholar] [CrossRef] - Hosseini Imani, M.; Zalzar, S.; Mosavi, A.; Shamshirband, S. Strategic behavior of retailers for risk reduction and profit increment via distributed generators and demand response programs. Energies
**2018**, 11, 1602. [Google Scholar] [CrossRef] - Dineva, A; Mosavi, A.; Ardabili, S.F.; Vajda, I.; Shamshirband, S.; Rabczuk, T.; Chau, K.-W. Review of soft computing models in design and control of rotating electrical machines. Energies
**2019**, 12, 1049. [Google Scholar] [CrossRef] - Chong, L.W.; Wong, Y.W.; Rajkumar, R.K.; Rajkumar, R.K.; Isa, D. Hybrid energy storage systems and control strategies for stand-alone renewable energy power systems. Renew. Sustain. Energy Rev.
**2016**, 66, 174–189. [Google Scholar] [CrossRef] - Curry, N.; Pillay, P. Biogas prediction and design of a food waste to energy system for the urban environment. Renew. Energy
**2012**, 41, 200–209. [Google Scholar] [CrossRef] - Amarasinghe, K.; Marino, D.L.; Manic, M. Deep neural networks for energy load forecasting. In Proceedings of the 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, UK, 19–21 June 2017; pp. 1483–1488. [Google Scholar]
- Quej, V.H.; Almorox, J.; Arnaldo, J.A.; Saito, L. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. J. Atmos. Sol.-Terr. Phys.
**2017**, 155, 62–70. [Google Scholar] [CrossRef] - Daut, M.A.M.; 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] - 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] - Kalogirou, S.A. Applications of artificial neural-networks for energy systems. Appl. Energy
**2000**, 67, 17–35. [Google Scholar] [CrossRef] - Faizollahzadeh Ardabili, S.; Najafi, B.; Alizamir, M.; Mosavi, A.; Shamshirband, S.; Rabczuk, T. Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters. Energies
**2018**, 11, 2889. [Google Scholar] [CrossRef] - Al-Jarrah, O.Y.; Yoo, P.D.; Muhaidat, S.; Karagiannidis, G.K.; Taha, K. Efficient machine learning for big data: A review. Big Data Res.
**2015**, 2, 87–93. [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] - Peng, Y.; Rysanek, A.; Nagy, Z.; Schlüter, A. Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Appl. Energy
**2018**, 211, 1343–1358. [Google Scholar] [CrossRef] - Tsanas, A.; Xifara, A. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build.
**2012**, 49, 560–567. [Google Scholar] [CrossRef] - Robinson, C.; Dilkina, B.; Hubbs, J.; Zhang, W.; Guhathakurta, S.; Brown, M.A.; Pendyala, R.M. Machine learning approaches for estimating commercial building energy consumption. Appl. Energy
**2017**, 208, 889–904. [Google Scholar] [CrossRef] - Voyant, C.; Notton, G.; Kalogirou, S.; Nivet, M.-L.; Paoli, C.; Motte, F.; Fouilloy, A. Machine learning methods for solar radiation forecasting: A review. Renew. Energy
**2017**, 105, 569–582. [Google Scholar] [CrossRef] - Mosavi, A.; Lopez, A.; Varkonyi-Koczy, A.R. Industrial applications of big data: State of the art survey. In Proceedings of the International Conference on Global Research and Education, Iași, Romania, 25–28 September 2017; pp. 225–232. [Google Scholar]
- Qasem, S.N.; Samadianfard, S.; Nahand, H.S.; Mosavi, A.; Shamshirband, S.; Chau, K.W. Estimating Daily Dew Point Temperature Using Machine Learning Algorithms. Water
**2019**, 11, 582. [Google Scholar] [CrossRef] - Najafi, B.; Ardabili, S.F. Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC). Resour. Conserv. Recycl.
**2018**, 133, 169–178. [Google Scholar] [CrossRef] - Faizollahzadeh Ardabili, S.; Najafi, B.; Ghaebi, H.; Shamshirband, S.; Mostafaeipour, A. A novel enhanced exergy method in analyzing HVAC system using soft computing approaches: A case study on mushroom growing hall. J. Build. Eng.
**2017**, 13, 309–318. [Google Scholar] [CrossRef] - Abbas, F.; Habib, S.; Feng, D.; Yan, Z. Optimizing Generation Capacities Incorporating Renewable Energy with Storage Systems Using Genetic Algorithms. Electronics
**2018**, 7, 100. [Google Scholar] [CrossRef] - Anwar, M.B.; El Moursi, M.S.; Xiao, W. Novel Power Smoothing and Generation Scheduling Strategies for a Hybrid Wind and Marine Current Turbine System. IEEE Trans. Power Syst.
**2017**, 32, 1315–1326. [Google Scholar] [CrossRef] - Boukelia, T.; Arslan, O.; Mecibah, M. Potential assessment of a parabolic trough solar thermal power plant considering hourly analysis: ANN-based approach. Renew. Energy
**2017**, 105, 324–333. [Google Scholar] [CrossRef] - Chatziagorakis, P.; Ziogou, C.; Elmasides, C.; Sirakoulis, G.C.; Karafyllidis, I.; Andreadis, I.; Georgoulas, N.; Giaouris, D.; Papadopoulos, A.I.; Ipsakis, D. Enhancement of hybrid renewable energy systems control with neural networks applied to weather forecasting: The case of Olvio. Neural Comput. Appl.
**2016**, 27, 1093–1118. [Google Scholar] [CrossRef] - Gallagher, C.V.; Bruton, K.; Leahy, K.; O’Sullivan, D.T. The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings. Energy Build.
**2018**, 158, 647–655. [Google Scholar] [CrossRef] - Faizollahzadeh Ardabili, S.; Mahmoudi, A.; Mesri Gundoshmian, T. Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers. J. Build. Eng.
**2016**, 6, 301–308. [Google Scholar] [CrossRef] - Karballaeezadeh, N.; Mohammadzadeh, S.D.; Shamshirband, S.; Hajikhodaverdikhan, P.; Mosavi, A.; Chau, K.W. Prediction of remaining service life of pavement using an optimized support vector machine. Eng. Appl. Comput. Fluid Mech.
**2019**, 13, 188–198. [Google Scholar] - Faizollahzadeh Ardabili, S.; Najafi, B.; Shamshirband, S.; Minaei Bidgoli, B.; Deo, R.C.; Chau, K.-w. Computational intelligence approach for modeling hydrogen production: A review. Eng. Appl. Comput. Fluid Mech.
**2018**, 12, 438–458. [Google Scholar] [CrossRef] - Ahmad, A.; Anderson, T.; Lie, T. Hourly global solar irradiation forecasting for New Zealand. Sol. Energy
**2015**, 122, 1398–1408. [Google Scholar] [CrossRef][Green Version] - Chahkoutahi, F.; Khashei, M. A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting. Energy
**2017**, 140, 988–1004. [Google Scholar] [CrossRef] - Kazem, H.A.; Yousif, J.H. Comparison of prediction methods of photovoltaic power system production using a measured dataset. Energy Convers. Manag.
**2017**, 148, 1070–1081. [Google Scholar] [CrossRef] - Loutfi, H.; Bernatchou, A.; Tadili, R. Generation of Horizontal Hourly Global Solar Radiation From Exogenous Variables Using an Artificial Neural Network in Fes (Morocco). Int. J. Renew. Energy Res. (IJRER)
**2017**, 7, 1097–1107. [Google Scholar] - Shimray, B.A.; Singh, K.; Khelchandra, T.; Mehta, R. Ranking of Sites for Installation of Hydropower Plant Using MLP Neural Network Trained with GA: A MADM Approach. Comput. Intell. Neurosci.
**2017**, 2017, 4152140. [Google Scholar] [CrossRef] [PubMed] - Arat, H.; Arslan, O. Optimization of district heating system aided by geothermal heat pump: A novel multistage with multilevel ANN modelling. Appl. Therm. Eng.
**2017**, 111, 608–623. [Google Scholar] [CrossRef] - Bagnasco, A.; Fresi, F.; Saviozzi, M.; Silvestro, F.; Vinci, A. Electrical consumption forecasting in hospital facilities: An application case. Energy Build.
**2015**, 103, 261–270. [Google Scholar] [CrossRef] - Li, Q.; Wu, Z.; Xia, X. Estimate and characterize PV power at demand-side hybrid system. Appl. Energy
**2018**, 218, 66–77. [Google Scholar] [CrossRef] - Premalatha, N.; Valan Arasu, A. Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. J. Appl. Res. Technol.
**2016**, 14, 206–214. [Google Scholar] [CrossRef][Green Version] - Yaïci, W.; Entchev, E. Performance prediction of a solar thermal energy system using artificial neural networks. Appl. Therm. Eng.
**2014**, 73, 1348–1359. [Google Scholar] [CrossRef] - Arabloo, M.B.A.; Ghiasi, M.M.; Lee, M.; Abbas, A.; Zendehboudi, S. A novel modeling approach to optimize oxygen-steam ratios in coal gasification process. Fuel
**2015**, 153. [Google Scholar] [CrossRef] - Arikan, C.; Ozdemir, M. Classification of power quality disturbances at power system frequency and out of power system frequency using support vector machines. Prz. Elektrotech
**2013**, 89, 284–291. [Google Scholar] - Ma, J.; Jiang, H.; Huang, K.; Bi, Z.; Man, K.L. Novel Field-Support Vector Regression-Based Soft Sensor for Accurate Estimation of Solar Irradiance. IEEE Trans. Circuits Syst. I Regul. Pap.
**2017**, 64, 3183–3191. [Google Scholar] [CrossRef] - Özdemir, S.; Demirtas, M.; Aydin, S. Harmonic estimation based support vector machine for typical power systems. Neural Netw. World
**2016**, 26, 233–252. [Google Scholar] [CrossRef] - Pinto, T.; Sousa, T.M.; Praça, I.; Vale, Z.; Morais, H. Support Vector Machines for decision support in electricity markets’ strategic bidding. Neurocomputing
**2016**, 172, 438–445. [Google Scholar] [CrossRef] - Zhang, J.; Walter, G.G.; Miao, Y.; Lee, W.N.W. Wavelet neural networks for function learning. IEEE Trans. Signal Process.
**1995**, 43, 1485–1497. [Google Scholar] [CrossRef] - Doucoure, B.; Agbossou, K.; Cardenas, A. Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data. Renew. Energy
**2016**, 92, 202–211. [Google Scholar] [CrossRef] - Gu, J.; Wang, J.; Qi, C.; Min, C.; Sundén, B. Medium-term heat load prediction for an existing residential building based on a wireless on-off control system. Energy
**2018**, 152, 709–718. [Google Scholar] [CrossRef] - He, Q.; Wang, J.; Lu, H. A hybrid system for short-term wind speed forecasting. Appl. Energy
**2018**, 226, 756–771. [Google Scholar] [CrossRef] - Qin, F.; Li, W.; Hu, Y.; Xu, G. An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming. Algorithms
**2018**, 11, 33. [Google Scholar] [CrossRef] - Sarshar, J.; Moosapour, S.S.; Joorabian, M. Multi-objective energy management of a micro-grid considering uncertainty in wind power forecasting. Energy
**2017**, 139, 680–693. [Google Scholar] [CrossRef] - Hadi Abdulwahid, A.; Wang, S. A Novel Method of Protection to Prevent Reverse Power Flow Based on Neuro-Fuzzy Networks for Smart Grid. Sustainability
**2018**, 10, 1059. [Google Scholar] [CrossRef] - Bassam, A.; May Tzuc, O.; Escalante Soberanis, M.; Ricalde, L.; Cruz, B. Temperature estimation for photovoltaic array using an adaptive neuro fuzzy inference system. Sustainability
**2017**, 9, 1399. [Google Scholar] [CrossRef] - Kampouropoulos, K.; Andrade, F.; Sala, E.; Espinosa, A.G.; Romeral, L. Multiobjective optimization of multi-carrier energy system using a combination of ANFIS and genetic algorithms. IEEE Trans. Smart Grid
**2018**, 9, 2276–2283. [Google Scholar] [CrossRef] - Mohammadi, K.; Shamshirband, S.; Kamsin, A.; Lai, P.; Mansor, Z. Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure. Renew. Sustain. Energy Rev.
**2016**, 63, 423–434. [Google Scholar] [CrossRef] - Sajjadi, B.; Raman, A.A.A.; Parthasarathy, R.; Shamshirband, S. Sensitivity analysis of catalyzed-transesterification as a renewable and sustainable energy production system by adaptive neuro-fuzzy methodology. J. Taiwan Inst. Chem. Eng.
**2016**, 64, 47–58. [Google Scholar] [CrossRef] - Nosratabadi, S.; Mosavi, A.; Shamshirband, S.; Kazimieras Zavadskas, E.; Rakotonirainy, A.; Chau, K.W. Sustainable Business Models: A Review. Sustainability
**2019**, 11, 1663. [Google Scholar] [CrossRef] - Aguado, J.A.; Racero, A.J.S.; de la Torre, S. Optimal operation of electric railways with renewable energy and electric storage systems. IEEE Trans. Smart Grid
**2018**, 9, 993–1001. [Google Scholar] [CrossRef] - Costa, D.C.; Nunes, M.V.; Vieira, J.P.; Bezerra, U.H. Decision tree-based security dispatch application in integrated electric power and natural-gas networks. Electr. Power Syst. Res.
**2016**, 141, 442–449. [Google Scholar] [CrossRef] - Kamali, S.; Amraee, T. Blackout prediction in interconnected electric energy systems considering generation re-dispatch and energy curtailment. Appl. Energy
**2017**, 187, 50–61. [Google Scholar] [CrossRef] - Moutis, P.; Skarvelis-Kazakos, S.; Brucoli, M. Decision tree aided planning and energy balancing of planned community microgrids. Appl. Energy
**2016**, 161, 197–205. [Google Scholar] [CrossRef][Green Version] - Ottesen, S.Ø.; Tomasgard, A.; Fleten, S.-E. Prosumer bidding and scheduling in electricity markets. Energy
**2016**, 94, 828–843. [Google Scholar] [CrossRef][Green Version] - Chemali, E.; Kollmeyer, P.J.; Preindl, M.; Emadi, A. State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach. J. Power Sources
**2018**, 400, 242–255. [Google Scholar] [CrossRef] - Coelho, I.M.; Coelho, V.N.; Luz, E.J.d.S.; Ochi, L.S.; Guimarães, F.G.; Rios, E. A GPU deep learning metaheuristic based model for time series forecasting. Appl. Energy
**2017**, 201, 412–418. [Google Scholar] [CrossRef] - Kim, J.; Le, T.-T.-H.; Kim, H. Nonintrusive load monitoring based on advanced deep learning and novel signature. Comput. Intell. Neurosci.
**2017**, 2017. [Google Scholar] [CrossRef] [PubMed] - Mocanu, E.; Nguyen, P.H.; Gibescu, M.; Kling, W.L. Deep learning for estimating building energy consumption. Sustain. Energy Grids Netw.
**2016**, 6, 91–99. [Google Scholar] [CrossRef] - Wang, H.; Yi, H.; Peng, J.; Wang, G.; Liu, Y.; Jiang, H.; Liu, W. Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network. Energy Convers. Manag.
**2017**, 153, 409–422. [Google Scholar] [CrossRef] - Liu, Y.; Yao, X. Ensemble learning via negative correlation. Neural Netw.
**1999**, 12, 1399–1404. [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] - Fu, G. Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system. Energy
**2018**, 148, 269–282. [Google Scholar] [CrossRef] - Gjoreski, H.; Kaluža, B.; Gams, M.; Milić, R.; Luštrek, M. Context-based ensemble method for human energy expenditure estimation. Appl. Soft Comput.
**2015**, 37, 960–970. [Google Scholar] [CrossRef] - Hasan, A.N.; Twala, B. Mine’s Pump Station Energy consumption and Underground Water Dam Levels Monitoring System Using Machine Learning Classifiers and Mutual Information Ensemble Technique. Int. J. Innov. Comput. Inf. CONTROL
**2016**, 12, 1777–1789. [Google Scholar] - Fotovatikhah, F.; Herrera, M.; Shamshirband, S.; Chau, K.-w.; Faizollahzadeh Ardabili, S.; Piran, M.J. Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Eng. Appl. Comput. Fluid Mech.
**2018**, 12, 411–437. [Google Scholar] [CrossRef] - Deng, B.; Peng, D.; Zhang, H.; Qian, Y. An intelligent hybrid short-term load forecasting model optimized by switching delayed PSO of micro-grids. J. Renew. Sustain. Energy
**2018**, 10, 024901. [Google Scholar] [CrossRef] - Dou, C.-X.; An, X.-G.; Yue, D. Multi-agent system based energy management strategies for microgrid by using renewable energy source and load forecasting. Electr. Power Compon. Syst.
**2016**, 44, 2059–2072. [Google Scholar] [CrossRef] - Peng, L.-L.; Fan, G.-F.; Huang, M.-L.; Hong, W.-C. Hybridizing DEMD and quantum PSO with SVR in electric load forecasting. Energies
**2016**, 9, 221. [Google Scholar] [CrossRef] - Qu, Z.; Zhang, K.; Wang, J.; Zhang, W.; Leng, W. A hybrid model based on ensemble empirical mode decomposition and fruit fly optimization algorithm for wind speed forecasting. Adv. Meteorol.
**2016**, 2016. [Google Scholar] [CrossRef] - Yang, Z.; Ce, L.; Lian, L. Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Appl. Energy
**2017**, 190, 291–305. [Google Scholar] [CrossRef] - Dehghani, M.; Riahi-Madvar, H.; Hooshyaripor, F.; Mosavi, A.; Shamshirband, S.; Zavadskas, E.K.; Chau, K.-W. Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System. Energies
**2019**, 12, 289. [Google Scholar] [CrossRef] - Mosavi, A.; Rituraj, R.; Varkonyi-Koczy, A.R. Review on the Usage of the Multiobjective Optimization Package of modeFrontier in the Energy Sector. In Proceedings of the International Conference on Global Research and Education, Iași, Romania, 25–28 September 2017; pp. 217–224. [Google Scholar]
- Moeini, I.; Ahmadpour, M.; Mosavi, A.; Alharbi, N.; Gorji, N.E. Modeling the time-dependent characteristics of perovskite solar cells. Sol. Energy
**2018**, 170, 969–973. [Google Scholar] [CrossRef] - Mosavi, A.; Torabi, M.; Ozturk, P.; Varkonyi-Koczy, A.; Istvan, V. A hybrid machine learning approach for daily prediction of solar radiation. In Proceedings of the International Conference on Global Research and Education, Kaunas, Lithuania, 24–27 September 2018. [Google Scholar]
- Ijadi Maghsoodi, A.; Ijadi Maghsoodi, A.; Mosavi, A.; Rabczuk, T.; Zavadskas, E. Renewable Energy Technology Selection Problem Using Integrated H-SWARA-MULTIMOORA Approach. Sustainability
**2018**, 10, 4481. [Google Scholar] [CrossRef] - David, M.; Ramahatana, F.; Trombe, P.-J.; Lauret, P. Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models. Sol. Energy
**2016**, 133, 55–72. [Google Scholar] [CrossRef][Green Version] - Feng, Y.; Cui, N.; Zhang, Q.; Zhao, L.; Gong, D. Comparison of artificial intelligence and empirical models for estimation of daily diffuse solar radiation in North China Plain. Int. J. Hydrogen Energy
**2017**, 42, 14418–14428. [Google Scholar] [CrossRef] - Hassan, M.A.; Khalil, A.; Kaseb, S.; Kassem, M. Exploring the potential of tree-based ensemble methods in solar radiation modeling. Appl. Energy
**2017**, 203, 897–916. [Google Scholar] [CrossRef] - Salcedo-Sanz, S.; Deo, R.C.; Cornejo-Bueno, L.; Camacho-Gómez, C.; Ghimire, S. An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia. Appl. Energy
**2018**, 209, 79–94. [Google Scholar] [CrossRef] - Salcedo-Sanz, S.; Jiménez-Fernández, S.; Aybar-Ruiz, A.; Casanova-Mateo, C.; Sanz-Justo, J.; García-Herrera, R. A CRO-species optimization scheme for robust global solar radiation statistical downscaling. Renew. Energy
**2017**, 111, 63–76. [Google Scholar] [CrossRef] - Touati, F.; Chowdhury, N.A.; Benhmed, K.; Gonzales, A.J.S.P.; Al-Hitmi, M.A.; Benammar, M.; Gastli, A.; Ben-Brahim, L. Long-term performance analysis and power prediction of PV technology in the State of Qatar. Renew. Energy
**2017**, 113, 952–965. [Google Scholar] [CrossRef] - Voyant, C.; Motte, F.; Fouilloy, A.; Notton, G.; Paoli, C.; Nivet, M.-L. Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies. Energy
**2017**, 120, 199–208. [Google Scholar] [CrossRef] - Voyant, C.; Notton, G.; Darras, C.; Fouilloy, A.; Motte, F. Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case. Energy
**2017**, 125, 248–257. [Google Scholar] [CrossRef] - Cornejo-Bueno, L.; Cuadra, L.; Jiménez-Fernández, S.; Acevedo-Rodríguez, J.; Prieto, L.; Salcedo-Sanz, S. Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data. Energies
**2017**, 10, 1784. [Google Scholar] [CrossRef] - Khosravi, A.; Machado, L.; Nunes, R. Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil. Appl. Energy
**2018**, 224, 550–566. [Google Scholar] [CrossRef] - Burlando, M.; Meissner, C. Evaluation of Two ANN Approaches for the Wind Power Forecast in a Mountainous Site. Int. J. Renew. Energy Res. (IJRER)
**2017**, 7, 1629–1638. [Google Scholar] - Pandit, R.; Infield, D. Gaussian process operational curves for wind turbine condition monitoring. Energies
**2018**, 11, 1631. [Google Scholar] [CrossRef] - Sharifian, A.; Ghadi, M.J.; Ghavidel, S.; Li, L.; Zhang, J. A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data. Renew. Energy
**2018**, 120, 220–230. [Google Scholar] [CrossRef] - Baranyai, M.; Mosavi, A.; Vajda, I.; Varkonyi-Koczy, A.R. Optimal Design of Electrical Machines: State of the Art Survey. In Proceedings of the International Conference on Global Research and Education, Iași, Romania, 25–28 September 2017; pp. 209–216. [Google Scholar]
- Albert, A.; Maasoumy, M. Predictive segmentation of energy consumers. Appl. Energy
**2016**, 177, 435–448. [Google Scholar] [CrossRef] - Alobaidi, M.H.; Chebana, F.; Meguid, M.A. Robust ensemble learning framework for day-ahead forecasting of household based energy consumption. Appl. Energy
**2018**, 212, 997–1012. [Google Scholar] [CrossRef] - Benedetti, M.; Cesarotti, V.; Introna, V.; Serranti, J. Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study. Appl. Energy
**2016**, 165, 60–71. [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] - Kuroha, R.; Fujimoto, Y.; Hirohashi, W.; Amano, Y.; Tanabe, S.-I.; Hayashi, Y. Operation planning method for home air-conditioners considering characteristics of installation environment. Energy Build.
**2018**, 177, 351–362. [Google Scholar] [CrossRef] - Wang, N.; Vlachokostas, A.; Borkum, M.; Bergmann, H.; Zaleski, S. Unique Building Identifier: A natural key for building data matching and its energy applications. Energy Build.
**2019**, 184, 230–241. [Google Scholar] [CrossRef] - Depecker, P.; Menezo, C.; Virgone, J.; Lepers, S. Design of buildings shape and energetic consumption. Build. Environ.
**2001**, 36, 627–635. [Google Scholar] [CrossRef] - Qi, F.; Wang, Y. A new calculation method for shape coefficient of residential building using Google Earth. Energy Build.
**2014**, 76, 72–80. [Google Scholar] [CrossRef] - Livingston, O.V.; Pulsipher, T.C.; Anderson, D.M.; Vlachokostas, A.; Wang, N. An analysis of utility meter data aggregation and tenant privacy to support energy use disclosure in commercial buildings. Energy
**2018**, 159, 302–309. [Google Scholar] [CrossRef] - Sweeney, L. k-anonymity: A model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst.
**2002**, 10, 557–570. [Google Scholar] [CrossRef] - Perez, A.J.; Zeadally, S. PEAR: A privacy-enabled architecture for crowdsensing. In Proceedings of the International Conference on Research in Adaptive and Convergent Systems, Krakow, Poland, 20–23 September 2017; pp. 166–171. [Google Scholar]

**Figure 4.**Results for study by Abbas et al. (2018). Reproduced from [29], Elsevier: 2018.

**Figure 7.**RMSE values for study by Chahkoutahi and Khashei (2017). Reproduced from [38], Elsevier 2017.

**Figure 11.**Correlation coefficient values for the study by Li et al. (2018). Reproduced from [44], Elsevier: 2018.

**Figure 16.**(

**a**) RMSE and (

**b**) correlation coefficient values for study by Bassam et al. (2017). Reproduced from [59], Elsevier: 2017.

**Figure 18.**(

**a**) RMSE and (

**b**) correlation coefficient values for study by Mocanu et al. (2016). Reproduced from [72], Elsevier: 2016.

**Figure 26.**(

**a**) RMSE and (

**b**) r values for study by Feng et al. (2017). Reproduced from [91], Elsevier: 2017.

**Figure 27.**(

**a**) RMSE and (

**b**) r values for study by Hassan et al. (2017). Reproduced from [92], Elsevier: 2017.

**Figure 28.**(

**a**) RMSE and (

**b**) r values for study by Salcedo-Sanz et al. (2018). Reproduced from [93], Elsevier: 2018.

**Figure 29.**RMSE values for study by Salcedo-Sanz et al. (2017). Reproduced from [94], Elsevier: 2017.

**Figure 30.**Correlation coefficient values for study by Touati et al. (2017). Reproduced from [95], the Elsevier: 2017.

**Figure 31.**RMSE values for study by Cornejo-Bueno et al. (2017). Reproduced from [98], Elsevier: 2017.

**Figure 32.**(

**a**) RMSE and (

**b**) r values for study by Khosravi et al. (2018). Reproduced from [99], Elsevier: 2018.

**Figure 33.**Correlation coefficient values for study by Pandit and Infield (2018). Reproduced from [101], Elsevier: 2018.

Year | Reference | Journal | Application |
---|---|---|---|

2018 | Abbas et al. [29] | Electronics (Switzerland) | Optimization of renewable energy generation capacities |

2017 | Anwar et al. [30] | IEEE Transactions on Power Systems | Mitigation of wind power fluctuation and scheduling strategies for power generation |

2017 | Boukelia et al. [31] | Renewable Energy | Prediction of levelized cost of electricity |

2016 | Chatziagorakis et al. [32] | Neural Computing and Applications | A forecasting model for wind speed and hourly and daily solar radiation |

2018 | Gallagher et al. [33] | Energy and Buildings | Measurement and verification of energy savings in industrial buildings |

Year | Reference | Journal | Application |
---|---|---|---|

2015 | Ahmad et al. [37] | Solar Energy | A day ahead prediction of hourly global solar irradiation |

2017 | Chahkoutahi et al. [38] | Energy | Electricity load forecasting |

2017 | Kazem et al. [39] | Energy Conversion and Management | Prediction of solar system power output |

2017 | Loutfi et al. [40] | International Journal of Renewable Energy Research | Hourly global solar radiation prediction |

2017 | Shimray et al. [41] | Computational Intelligence and Neuroscience | Ranking of different potential power plant projects |

**Table 3.**Results of evaluations of models by Loutfi et al. (2017). Reproduced from [40], Elsevier: 2017.

ML Model | The Best Structure | r | f |
---|---|---|---|

MLP | 5-30-1 | 0.938 | 23.31 |

NARX | 1 5-10-1 | 0.974 | 15.1 |

Year | Reference | Journal | Application |
---|---|---|---|

2017 | Arat and Arslan [42] | Applied Thermal Engineering | Optimization of the district heating system aided with geothermal heat pump |

2015 | Bagnasco et al. [43] | Energy and Buildings | Electrical consumption forecasting model of a building |

2018 | Li, Q et al. [44] | Applied Energy | Forecasting of PV power generation |

2016 | Premalatha and Valan Arasu [45] | Journal of Applied Research and Technology | Monthly average global radiation prediction |

2014 | Yaïci and Entchev [46] | Applied Thermal Engineering | Performance forecasting of a solar thermal energy system |

Parameter | Model | RMSE | Parameter | Model | RMSE | Parameter | Model | RMSE |
---|---|---|---|---|---|---|---|---|

COP | LM-28 | 0.07506 | Cinst | LM-28 | 229,835 | WP2 | LM-50 | 58.3957 |

SCG-28 | 0.79114 | SCG-28 | 335,283 | SCG-50 | 606.962 | |||

CGP-28 | 0.77328 | CGP-28 | 345,108.6 | CGP-50 | 862.943 | |||

εsys | LM-28 | 0.00335 | Cop | LM-28 | 32,357.74 | WP3 | LM-50 | 1.14269 |

SCG-28 | 0.01616 | SCG-28 | 183,385.5 | SCG-50 | 13.3365 | |||

CGP-28 | 0.01963 | CGP-28 | 384,134.8 | CGP-50 | 22.8781 | |||

Wc | LM-50 | 98.07454 | NPV | LM-20 | 45,456.24 | Ηr | LM-50 | 25.2462 |

SCG-50 | 1044.513 | SCG-20 | 3,104,498 | SCG-50 | 294.616 | |||

CGP-50 | 1939.327 | CGP-20 | 20,565,006 | CGP-50 | 498.103 | |||

Qcon | LM-50 | 87.0961 | COPsys | LM-28 | 0.03824 | Qevp | LM-50 | 2.208349 |

SCG-50 | 1027.196 | SCG-28 | 0.31434 | SCG-50 | 465.1691 | |||

CGP-50 | 1754.37 | CGP-28 | 0.29927 | CGP-50 | 575.9355 |

Year | Authors | Journal | Application |
---|---|---|---|

2015 | Arabloo et al. [47] | Fuel | Estimation of optimum oxygen-steam ratios |

2013 | Arikan et al. [48] | International Review of Electrical Engineering | Classification of power quality disturbances |

2017 | Ma et al. [49] | IEEE Transactions on Circuits and Systems I: Regular Papers | Estimation of irradiance levels from photovoltaic electrical characteristics |

2016 | Özdemir et al. [50] | Neural Network World | Harmonic estimation of power quality in electrical energy systems |

2016 | Pinto et al. [51] | Neurocomputing | An electricity market price prediction in a fast execution time |

Year | Authors | Journal | Application |
---|---|---|---|

2016 | Doucoure et al. [53] | Renewable Energy | Prediction of time series for renewable energy sources |

2018 | Gu et al. [54] | Energy | Heat load perdition in district heating systems |

2018 | He et al. [55] | Applied Energy | Wind speed forecasting (reduction of the influence of noise in the raw data series) |

2018 | Qin et al. [56] | Algorithms | Simultaneous optimization of fuel economy and battery state of charge |

2017 | Sarshar et al. [57] | Energy | Uncertainty reduction in wind power prediction |

Year | Reference | Journal | Application |
---|---|---|---|

2018 | Abdulwahid et al. [58] | Sustainability (Switzerland) | A protection device for a reverse power protection system |

2017 | Bassam et al. [59] | Sustainability (Switzerland) | Module temperature estimation of PV systems |

2018 | Kampouropoulos et al. [60] | IEEE Transactions on Smart Grid | Prediction of the power demand of a plant and optimization of energy flow |

2016 | Mohammadi et al. [61] | Renewable and Sustainable Energy Reviews | Identification of the most relevant parameters for forecasting of daily global solar radiation |

2016 | Sajjadi et al. [62] | Journal of the Taiwan Institute of Chemical Engineers | Transesterification yield estimation and prediction of biodiesel synthesis |

Year | Authors | Journal | Application |
---|---|---|---|

2018 | Aguado et al. [64] | IEEE Transactions on Smart Grid | Railway electric energy systems optimal operation |

2016 | Costa et al. [65] | Electric Power Systems Research | Security dispatch method for coupled natural gas and electric power networks |

2017 | Kamali et al. [66] | Applied Energy | Prediction of the risk of a blackout in electric energy systems |

2016 | Moutis et al. [67] | Applied Energy | energy storage planning and energy controlling |

2016 | Ottesen [68] | Energy | Total cost minimization in energy systems for the prosumers’ buildings |

Year | Authors | Journal | Application |
---|---|---|---|

2018 | Chemali et al. [69] | Journal of Power Sources | Battery State-of-charge estimation |

2017 | Coelho et al. [70] | Applied Energy | Household electricity demand forecasting |

2017 | Kim et al. [71] | Computational Intelligence and Neuroscience | Estimation of the power consumption of individual appliances in the distribution system |

2016 | Mocanu et al. [72] | Sustainable Energy, Grids, and Networks | Prediction of building energy consumption |

2017 | Wang et al. [73] | Energy Conversion and Management | PV power forecasting |

Year | Authors | Journal | Application |
---|---|---|---|

2015 | Burger and Moura [75] | Energy and Buildings | forecasting of building electricity demand |

2017 | Changfeng et al. | International Journal of Control and Automation | Non-linear fault features extraction |

2018 | Fu, G. [76] | Energy | Cooling load forecasting in buildings |

2015 | Gjoreski et al. [77] | Applied Soft Computing Journal | Human energy expenditure estimation |

2016 | Hasan and Twala [78] | International Journal of Innovative Computing, Information, and Control | Prediction of the underground water dam level |

Year | Reference | Journal | Application |
---|---|---|---|

2018 | Deng et al. [80] | Journal of Renewable and Sustainable Energy | Short-term load forecasting in microgrids |

2016 | Dou et al. [81] | Electric Power Components and Systems | prediction of renewable energy loads in microgrids |

2016 | Peng et al. [82] | Energies | Electric load forecasting |

2016 | Qu et al. [83] | Advances in Meteorology | Reliable wind speed prediction |

2017 | Yang and Lian [84] | Applied Energy | Electricity price prediction |

2019 | Dehghani et al. [85] | Energies | Hydropower generation forecasting |

2017 | Mosavi et al. [86] | Intelligent Systems | General energy sectors |

Year | Reference | Journal | ML Model | Application |
---|---|---|---|---|

2016 | David et al. [90] | Solar Energy | Hybrid ARMA-GARCH model | Forecasting of the solar irradiance |

2017 | Feng et al. [91] | International Journal of Hydrogen Energy | GRNN, RF, ELM and optimized back propagation GANN | Estimating daily Hd |

2017 | Hassan et al. [92] | Applied Energy | Gradient boosting, RF and bagging | Modeling solar radiation |

2018 | Salcedo-Sanz et al. [93] | Applied Energy | A hybrid CRO-ELM model. | Estimation of daily global solar radiation in Queensland, Australia. |

2017 | Salcedo-Sanz et al. [94] | Renewable Energy | A hybrid CCRO-ELM model. | Global solar radiation prediction at a given point |

2017 | Touati et al. [95] | Renewable Energy | Hybrid of MA, AR and ARMA modeling | Forecasting the output power of PV panels in environmental conditions. |

2017 | Voyant et al. [96] | Energy | linear quadratic estimation | Prediction of solar yields |

2017 | Voyant et al. [97] | Energy | Hybrid of multilayer perceptron | Forecasting of global radiation time series |

**Table 14.**Results related to the study by David et al. (2016). Reproduced from [90], Elsevier 2016.

Method | RMSE |
---|---|

Recursive ARMA | 20.8% |

SVR | 20.8% |

NN | 20.6% |

AR | 21.3% |

**Table 15.**Results related to the study by Feng et al. (2017). Reproduced from [91], Elsevier: 2017.

Station | ML Model | RMSE | r |
---|---|---|---|

Beijing | ELM | 17.3 | 0.9196 |

GANN | 17.1 | 0.9209 | |

RF | 18.3 | 0.9102 | |

GRNN | 19.2 | 0.8902 | |

Iqbal | 32.9 | 0.8865 | |

Zhengzhou | ELM | 13.8 | 0.947 |

GANN | 13.4 | 0.9515 | |

RE | 15 | 0.9408 | |

GRNN | 16.5 | 0.9072 | |

Iqbal | 35.8 | 0.929 |

Year | Reference | Journal | ML model | Application |
---|---|---|---|---|

2017 | Cornejo-Bueno et al. [98] | Energies | SVR, MLP and ELM, GPs, ERA-Interim reanalysis | Accurate prediction of Wind Power Ramp Events |

2018 | Khosravi et al. [99] | Applied Energy | MLP, SVR, fuzzy inference system, ANFIS, and group model of data handling | Prediction of wind speed data for Osorio wind farm |

2017 | Burlando et al. [100] | International Journal of Renewable Energy Research | Hybrids of ANNs | Accurate wind power forecast |

2018 | Pandit et al. [101] | Energies | Hybrid of GP | Predictive condition monitoring |

2018 | Sharifian et al. [102] | Renewable Energy | PSO, the Type-2 fuzzy NN | The wind power accurate forecasting |

Year | Reference | Journal | ML Model | Application |
---|---|---|---|---|

2016 | Albert and Maasoumy [104] | Applied Energy | predictive segmentation technique | Predictive segmentation technique for energy companies |

2018 | Alobaidi et al. [105] | Applied Energy | Hybrids | Predicting the average daily energy consumption on a household level |

2016 | Benedetti et al. [106] | Applied Energy | Hybrids of ANN | Control of energy consumption in energy-intensive industries |

2018 | Chen et al. [107] | Energy | ensemble learning technique (feedforward deep networks and extreme gradient boosting forest) | Prediction of the household electricity consumption |

2018 | Kuroha et al. [108] | Energy and Buildings | Support Vector Regression, Particle Swarm Optimization, Predicted Mean Vote | Improving thermal comfort and reduction of electricity costs |

2018 | Torabi et al. [7,88] | Sustainable Energy | Hybrid ML models | Solar radiation forecasting |

ML Model | Complexity | User-Friendliness | Accuracy | Speed | Dataset Type |
---|---|---|---|---|---|

ANN | Reasonably high | Low | High | Reasonable | Historical |

MLP | Reasonable | Reasonable | Reasonably high | High | Historical |

ELM | Reasonable | Reasonably high | Reasonable | Reasonably high | Historical |

SVM | Reasonably high | Low | High | Low | Historical |

DT | Reasonable | Low | Reasonable | Reasonable | Historical |

DL | High | Reasonable | High | Reasonable | Historical |

Ensemble | High | Low | Reasonable | High | Historical |

WNN | Reasonable | Low | High | Low | Historical |

ANFIS | Reasonable | Reasonable | Reasonable | High | Historical |

Hybrids | Reasonable | High | High | High | Historical |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

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 Systems, a Systematic Review. *Energies* **2019**, *12*, 1301.
https://doi.org/10.3390/en12071301

**AMA Style**

Mosavi A, Salimi M, Faizollahzadeh Ardabili S, Rabczuk T, Shamshirband S, Varkonyi-Koczy AR. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. *Energies*. 2019; 12(7):1301.
https://doi.org/10.3390/en12071301

**Chicago/Turabian Style**

Mosavi, Amir, Mohsen Salimi, Sina Faizollahzadeh Ardabili, Timon Rabczuk, Shahaboddin Shamshirband, and Annamaria R. Varkonyi-Koczy. 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review" *Energies* 12, no. 7: 1301.
https://doi.org/10.3390/en12071301