Computational Intelligence on ShortTerm Load Forecasting: A Methodological Overview
Abstract
:1. Introduction
2. STLF Methodologies
2.1. SimilarPattern Method
2.2. Variable Selection Method
2.3. Hierarchical Forecasting
2.4. Weather Station Selection
3. Method Evaluation and Future Work
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
Nomenclature
ANN  Artificial Neural Network 
ARIMA  AutoRegressive Integrated Moving Average 
ARMA  AutoRegressive Moving Average 
CI  Computational Intelligence 
DTW  Dynamic Time Warping 
GEFC  Global Energy Forecasting Competition 
HSTLF  Hierarchical ShortTerm Load Forecasting 
STLF  ShortTerm Load Forecasting 
MI  Mutual Information 
RNN  Recurrent Neural Network 
SARIMA  Seasonal AutoRegressive Integrated Moving Average 
SVM  Support Vector Machine 
References
 Shahidehpour, M.; Yamin, H.; Li, Z. Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management; John Wiley & Sons: Hoboken, NJ, USA, 2003. [Google Scholar]
 Hong, T.; Fan, S. Probabilistic electric load forecasting: A tutorial review. Int. J. Forecast. 2016, 32, 914–938. [Google Scholar] [CrossRef]
 Amjady, N. Shortterm hourly load forecasting using timeseries modeling with peak load estimation capability. IEEE Trans. Power Syst. 2001, 16, 498–505. [Google Scholar] [CrossRef]
 Khotanzad, A.; AfkhamiRohani, R.; Lu, T.L.; Abaye, A.; Davis, M.; Maratukulam, D.J. ANNSTLFa neuralnetworkbased electric load forecasting system. IEEE Trans. Neural Netw. 1997, 8, 835–846. [Google Scholar] [CrossRef]
 Huang, S.J.; Shih, K.R. Shortterm load forecasting via ARMA model identification including nonGaussian process considerations. IEEE Trans. Power Syst. 2003, 18, 673–679. [Google Scholar] [CrossRef]
 Pappas, S.S.; Ekonomou, L.; Karamousantas, D.C.; Chatzarakis, G.; Katsikas, S.; Liatsis, P. Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models. Energy 2008, 33, 1353–1360. [Google Scholar] [CrossRef]
 Lee, Y.S.; Tong, L.I. Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowl.Based Syst. 2011, 24, 66–72. [Google Scholar] [CrossRef]
 Chakhchoukh, Y.; Panciatici, P.; Mili, L. Electric load forecasting based on statistical robust methods. IEEE Trans. Power Syst. 2011, 26, 982–991. [Google Scholar] [CrossRef]
 Chen, B.J.; Chang, M.W. Load forecasting using support vector machines: A study on EUNITE competition 2001. IEEE Trans. Power Syst. 2004, 19, 1821–1830. [Google Scholar] [CrossRef]
 Khosravi, A.; Nahavandi, S.; Creighton, D.; Srinivasan, D. Interval type2 fuzzy logic systems for load forecasting: A comparative study. IEEE Trans. Power Syst. 2012, 27, 1274–1282. [Google Scholar] [CrossRef]
 Fan, S.; Hyndman, R.J. Shortterm load forecasting based on a semiparametric additive model. IEEE Trans. Power Syst. 2012, 27, 134–141. [Google Scholar] [CrossRef]
 Mandal, P.; Senjyu, T.; Urasaki, N.; Funabashi, T. A neural network based severalhourahead electric load forecasting using similar days approach. Int. J. Electr. Power Energy Syst. 2006, 28, 367–373. [Google Scholar] [CrossRef]
 Moghram, I.; Rahman, S. Analysis and evaluation of five shortterm load forecasting techniques. IEEE Trans. Power Syst. 1989, 4, 1484–1491. [Google Scholar] [CrossRef]
 Raza, M.Q.; Khosravi, A. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sustain. Energy Rev. 2015, 50, 1352–1372. [Google Scholar] [CrossRef]
 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]
 Hippert, H.S.; Pedreira, C.E.; Souza, R.C. Neural networks for shortterm load forecasting: A review and evaluation. IEEE Trans. Power Syst. 2001, 16, 44–55. [Google Scholar] [CrossRef]
 Feinberg, E.A.; Genethliou, D. Load forecasting. In Applied Mathematics for Restructured Electric Power Systems; Springer: New York, NY, USA, 2005; pp. 269–285. [Google Scholar]
 Hong, T.; Wang, P.; White, L. Weather station selection for electric load forecasting. Int. J. Forecast. 2015, 31, 286–295. [Google Scholar] [CrossRef]
 Mu, Q.; Wu, Y.; Pan, X.; Huang, L.; Li, X. Shortterm load forecasting using improved similar days method. In Proceedings of the 2010 AsiaPacific Power and Energy Engineering Conference, Chengdu, China, 28–31 March 2010; pp. 1–4. [Google Scholar]
 Dudek, G. Pattern similaritybased methods for shortterm load forecasting–Part 1: Principles. Appl. Soft Comput. 2015, 37, 277–287. [Google Scholar] [CrossRef]
 Chen, Y.; Luh, P.B.; Guan, C.; Zhao, Y.; Michel, L.D.; Coolbeth, M.A.; Friedland, P.B.; Rourke, S.J. Shortterm load forecasting: similar daybased wavelet neural networks. IEEE Trans. Power Syst. 2010, 25, 322–330. [Google Scholar] [CrossRef]
 Senjyu, T.; Takara, H.; Uezato, K.; Funabashi, T. Onehourahead load forecasting using neural network. IEEE Trans. Power Syst. 2002, 17, 113–118. [Google Scholar] [CrossRef]
 Teeraratkul, T.; O’Neill, D.; Lall, S. Shapebased approach to household electric load curve clustering and prediction. IEEE Trans. Smart Grid 2018, 9, 5196–5206. [Google Scholar] [CrossRef]
 Iglesias, F.; Kastner, W. Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies 2013, 6, 579–597. [Google Scholar] [CrossRef]
 Seem, J.E. Pattern recognition algorithm for determining days of the week with similar energy consumption profiles. Energy Build. 2005, 37, 127–139. [Google Scholar] [CrossRef]
 Alvarez, F.M.; Troncoso, A.; Riquelme, J.C.; Ruiz, J.S.A. Energy time series forecasting based on pattern sequence similarity. IEEE Trans. Knowl. Data Eng. 2011, 23, 1230–1243. [Google Scholar] [CrossRef]
 Quilumba, F.L.; Lee, W.J.; Huang, H.; Wang, D.Y.; Szabados, R.L. Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities. IEEE Trans. Smart Grid 2015, 6, 911–918. [Google Scholar] [CrossRef]
 Liu, C.; Jin, Z.; Gu, J.; Qiu, C. Shortterm load forecasting using a long shortterm memory network. In Proceedings of the 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGTEurope), Torino, Italy, 26–29 September 2017; pp. 1–6. [Google Scholar]
 Kong, W.; Dong, Z.Y.; Hill, D.J.; Luo, F.; Xu, Y. Shortterm residential load forecasting based on resident behaviour learning. IEEE Trans. Power Syst. 2018, 33, 1087–1088. [Google Scholar] [CrossRef]
 Shi, H.; Xu, M.; Li, R. Deep learning for household load forecasting–a novel pooling deep RNN. IEEE Trans. Smart Grid 2018, 9, 5271–5280. [Google Scholar] [CrossRef]
 Zheng, H.; Yuan, J.; Chen, L. Shortterm load forecasting using EMDLSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies 2017, 10, 1168. [Google Scholar] [CrossRef]
 Sutskever, I.; Vinyals, O.; Le, Q.V. Sequence to sequence learning with neural networks. In Proceedings of the Advances in Neural Information Processing Systems; 2014; pp. 3104–3112. [Google Scholar]
 Marino, D.L.; Amarasinghe, K.; Manic, M. Building energy load forecasting using deep neural networks. In Proceedings of the IECON 201642nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 23–26 October 2016; pp. 7046–7051. [Google Scholar]
 Satish, B.; Swarup, K.; Srinivas, S.; Rao, A.H. Effect of temperature on short term load forecasting using an integrated ANN. Electr. Power Syst. Res. 2004, 72, 95–101. [Google Scholar] [CrossRef]
 Barman, M.; Choudhury, N.D.; Sutradhar, S. A regional hybrid GOASVM model based on similar day approach for shortterm load forecasting in Assam, India. Energy 2018, 145, 710–720. [Google Scholar] [CrossRef]
 Ghofrani, M.; Ghayekhloo, M.; Arabali, A.; Ghayekhloo, A. A hybrid shortterm load forecasting with a new input selection framework. Energy 2015, 81, 777–786. [Google Scholar] [CrossRef]
 Jin, C.H.; Pok, G.; Lee, Y.; Park, H.W.; Kim, K.D.; Yun, U.; Ryu, K.H. A SOM clustering pattern sequencebased next symbol prediction method for dayahead direct electricity load and price forecasting. Energy Convers. Manag. 2015, 90, 84–92. [Google Scholar] [CrossRef]
 Panapakidis, I.P. Clustering based dayahead and hourahead bus load forecasting models. Int. J. Electr. Power Energy Syst. 2016, 80, 171–178. [Google Scholar] [CrossRef]
 Goia, A.; May, C.; Fusai, G. Functional clustering and linear regression for peak load forecasting. Int. J. Forecast. 2010, 26, 700–711. [Google Scholar] [CrossRef]
 Mori, H.; Itagaki, T. A precondition technique with reconstruction of data similarity based classification for shortterm load forecasting. In Proceedings of the IEEE Power Engineering Society General Meeting, Denver, CO, USA, 6–10 June 2004; pp. 280–285. [Google Scholar]
 Verdú, S.V.; Garcia, M.O.; Senabre, C.; Marín, A.G.; Franco, F.G. Classification, filtering, and identification of electrical customer load patterns through the use of selforganizing maps. IEEE Trans. Power Syst. 2006, 21, 1672–1682. [Google Scholar] [CrossRef]
 Hyndman, R.J.; Athanasopoulos, G. Forecasting: Principles and Practice. 2018. Available online: https://otexts.org/fpp2/ (accessed on 28 November 2018).
 Lusis, P.; Khalilpour, K.R.; Andrew, L.; Liebman, A. Shortterm residential load forecasting: Impact of calendar effects and forecast granularity. Appl. Energy 2017, 205, 654–669. [Google Scholar] [CrossRef]
 Ceperic, E.; Ceperic, V.; Baric, A. A strategy for shortterm load forecasting by support vector regression machines. IEEE Trans. Power Syst. 2013, 28, 4356–4364. [Google Scholar] [CrossRef]
 Espinoza, M.; Joye, C.; Belmans, R.; De Moor, B. Shortterm load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series. IEEE Trans. Power Syst. 2005, 20, 1622–1630. [Google Scholar] [CrossRef]
 May, R.; Dandy, G.; Maier, H. Review of input variable selection methods for artificial neural networks. Artificial Neural NetworksMethodological Advances and Biomedical Applications. 2011. Available online: https://www.intechopen.com/ (accessed on 28 November 2018).
 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]
 Nedellec, R.; Cugliari, J.; Goude, Y. GEFCom2012: Electric load forecasting and backcasting with semiparametric models. Int. J. Forecast. 2014, 30, 375–381. [Google Scholar] [CrossRef]
 Xiao, J.; Li, Y.; Xie, L.; Liu, D.; Huang, J. A hybrid model based on selective ensemble for energy consumption forecasting in China. Energy 2018, 159, 534–546. [Google Scholar] [CrossRef]
 Hall, M.A. CorrelationBased feature selection of discrete and numeric class machine learning. In Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, CA, USA, 29 June–2 July 2000. [Google Scholar]
 Kouhi, S.; Keynia, F.; Ravadanegh, S.N. A new shortterm load forecast method based on neuroevolutionary algorithm and chaotic feature selection. Int. J. Electr. Power Energy Syst. 2014, 62, 862–867. [Google Scholar] [CrossRef]
 Estévez, P.A.; Tesmer, M.; Perez, C.A.; Zurada, J.M. Normalized mutual information feature selection. IEEE Trans. Neural Netw. 2009, 20, 189–201. [Google Scholar] [CrossRef] [PubMed]
 Wang, Z.; Cao, Y. Mutual information and nonfixed ANNs for daily peak load forecasting. In Proceedings of the 2006 IEEE PES Power Systems Conference and Exposition, Atlanta, GA, USA, 29 October–1 November 2006; pp. 1523–1527. [Google Scholar]
 Elattar, E.E.; Goulermas, J.; Wu, Q.H. Electric load forecasting based on locally weighted support vector regression. IEEE Trans. Syst. Man Cybern. Part C 2010, 40, 438–447. [Google Scholar] [CrossRef]
 Wi, Y.M.; Joo, S.K.; Song, K.B. Holiday load forecasting using fuzzy polynomial regression with weather feature selection and adjustment. IEEE Trans. Power Syst. 2012, 27, 596. [Google Scholar] [CrossRef]
 Schaffernicht, E.; Gross, H.M. Weighted mutual information for feature selection. In Proceedings of the International Conference on Artificial Neural Networks, Espoo, Finland, 14–17 June 2011; pp. 181–188. [Google Scholar]
 Reis, A.R.; Da Silva, A.A. Feature extraction via multiresolution analysis for shortterm load forecasting. IEEE Trans. Power Syst. 2005, 20, 189–198. [Google Scholar]
 Amjady, N.; Keynia, F. Shortterm load forecasting of power systems by combination of wavelet transform and neuroevolutionary algorithm. Energy 2009, 34, 46–57. [Google Scholar] [CrossRef]
 Hu, Z.; Bao, Y.; Xiong, T.; Chiong, R. Hybrid filter–wrapper feature selection for shortterm load forecasting. Eng. Appl. Artif. Intell. 2015, 40, 17–27. [Google Scholar] [CrossRef]
 Niu, D.; Wang, Y.; Wu, D.D. Power load forecasting using support vector machine and ant colony optimization. Expert Syst. Appl. 2010, 37, 2531–2539. [Google Scholar] [CrossRef]
 Lin, S.W.; Ying, K.C.; Chen, S.C.; Lee, Z.J. Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst. Appl. 2008, 35, 1817–1824. [Google Scholar] [CrossRef]
 Hu, Z.; Bao, Y.; Xiong, T. Comprehensive learning particle swarm optimization based memetic algorithm for model selection in shortterm load forecasting using support vector regression. Appl. Soft Comput. 2014, 25, 15–25. [Google Scholar] [CrossRef]
 Amjady, N.; Keynia, F.; Zareipour, H. Shortterm load forecast of microgrids by a new bilevel prediction strategy. IEEE Trans. Smart Grid 2010, 1, 286–294. [Google Scholar] [CrossRef]
 Sheikhan, M.; Mohammadi, N. Neuralbased electricity load forecasting using hybrid of GA and ACO for feature selection. Neural Comput. Appl. 2012, 21, 1961–1970. [Google Scholar] [CrossRef]
 Liang, Y.; Niu, D.; Hong, W.C. Short term load forecasting based on feature extraction and improved general regression neural network model. Energy 2019, 166, 653–663. [Google Scholar] [CrossRef]
 Santos, P.; Martins, A.; Pires, A. Designing the input vector to ANNbased models for shortterm load forecast in electricity distribution systems. Int. J. Electr. Power Energy Syst. 2007, 29, 338–347. [Google Scholar] [CrossRef][Green Version]
 Ghadimi, N.; Akbarimajd, A.; Shayeghi, H.; Abedinia, O. Two stage forecast engine with feature selection technique and improved metaheuristic algorithm for electricity load forecasting. Energy 2018, 161, 130–142. [Google Scholar] [CrossRef]
 Hong, W.C.; Dong, Y.; Lai, C.Y.; Chen, L.Y.; Wei, S.Y. SVR with hybrid chaotic immune algorithm for seasonal load demand forecasting. Energies 2011, 4, 960–977. [Google Scholar] [CrossRef]
 Hu, Z.; Bao, Y.; Chiong, R.; Xiong, T. Midterm interval load forecasting using multioutput support vector regression with a memetic algorithm for feature selection. Energy 2015, 84, 419–431. [Google Scholar] [CrossRef]
 Swarup, K.S.; Satish, B. Integrated ANN approach to forecast load. IEEE Comput. Appl. Power 2002, 15, 46–51. [Google Scholar] [CrossRef]
 Khotanzad, A.; AfkhamiRohani, R.; Maratukulam, D. ANNSTLFartificial neural network shortterm load forecastergeneration three. IEEE Trans. Power Syst. 1998, 13, 1413–1422. [Google Scholar] [CrossRef]
 Kalaitzakis, K.; Stavrakakis, G.; Anagnostakis, E. Shortterm load forecasting based on artificial neural networks parallel implementation. Electr. Power Syst. Res. 2002, 63, 185–196. [Google Scholar] [CrossRef]
 Zhang, Y.; Wang, J.; Zhao, T. Using Quadratic Programming to Optimally Adjust Hierarchical Load Forecasting. IEEE Trans. Power Syst. 2018. [Google Scholar] [CrossRef]
 Sun, X.; Luh, P.B.; Cheung, K.W.; Guan, W.; Michel, L.D.; Venkata, S.; Miller, M.T. An efficient approach to shortterm load forecasting at the distribution level. IEEE Trans. Power Syst. 2016, 31, 2526–2537. [Google Scholar] [CrossRef]
 Hong, T.; Shahidehpour, M. Load Forecasting Case Study; EISPC, US Department of Energy: Washington, DC, USA, 2015.
 Capasso, A.; Grattieri, W.; Lamedica, R.; Prudenzi, A. A bottomup approach to residential load modeling. IEEE Trans. Power Syst. 1994, 9, 957–964. [Google Scholar] [CrossRef]
 Stephen, B.; Tang, X.; Harvey, P.R.; Galloway, S.; Jennett, K.I. Incorporating practice theory in subprofile models for short term aggregated residential load forecasting. IEEE Trans. Smart Grid 2017, 8, 1591–1598. [Google Scholar] [CrossRef]
 Hyndman, R.J.; Ahmed, R.A.; Athanasopoulos, G.; Shang, H.L. Optimal combination forecasts for hierarchical time series. Comput. Stat. Data Anal. 2011, 55, 2579–2589. [Google Scholar] [CrossRef]
 Gamakumara, P.; Panagiotelis, A.; Athanasopoulos, G.; Hyndman, R.J. Probabilistic Forecasts in Hierarchical Time Series; Monash University: Melbourne, Australia, 2018. [Google Scholar]
 NoseFilho, K.; Lotufo, A.D.P.; Minussi, C.R. Shortterm multinodal load forecasting using a modified general regression neural network. IEEE Trans. Power Deliv. 2011, 26, 2862–2869. [Google Scholar] [CrossRef]
 Fan, S.; Methaprayoon, K.; Lee, W.J. Multiregion load forecasting for system with large geographical area. IEEE Trans. Ind. Appl. 2009, 45, 1452–1459. [Google Scholar] [CrossRef]
 Wang, Y.; Chen, Q.; Sun, M.; Kang, C.; Xia, Q. An ensemble forecasting method for the aggregated load with sub profiles. IEEE Trans. Smart Grid 2018, 9, 3906–3908. [Google Scholar] [CrossRef]
 Yang, Y. Combining forecasting procedures: some theoretical results. Econ. Theory 2004, 20, 176–222. [Google Scholar] [CrossRef]
 Hong, T.; Pinson, P.; Fan, S. Global energy forecasting competition 2012. Int. J. Forecast. 2014, 30, 357–363. [Google Scholar] [CrossRef]
 Xie, J.; Chen, Y.; Hong, T.; Laing, T.D. Relative humidity for load forecasting models. IEEE Trans. Smart Grid 2018, 9, 191–198. [Google Scholar] [CrossRef]
 Liu, B.; Nowotarski, J.; Hong, T.; Weron, R. Probabilistic load forecasting via quantile regression averaging on sister forecasts. IEEE Trans. Smart Grid 2017, 8, 730–737. [Google Scholar] [CrossRef]
 Charlton, N.; Singleton, C. A refined parametric model for short term load forecasting. Int. J. Forecast. 2014, 30, 364–368. [Google Scholar] [CrossRef]
 Lloyd, J.R. GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processes. Int. J. Forecast. 2014, 30, 369–374. [Google Scholar] [CrossRef][Green Version]
 Taieb, S.B.; Hyndman, R.J. A gradient boosting approach to the Kaggle load forecasting competition. Int. J. Forecast. 2014, 30, 382–394. [Google Scholar] [CrossRef][Green Version]
 Fujimoto, Y.; Kikusato, H.; Yoshizawa, S.; Kawano, S.; Yoshida, A.; Wakao, S.; Murata, N.; Amano, Y.; Tanabe, S.i.; Hayashi, Y. Distributed energy management for comprehensive utilization of residential photovoltaic outputs. IEEE Trans. Smart Grid 2018, 9, 1216–1227. [Google Scholar] [CrossRef]
Paper  Method  Formulae  Parameters 

[21]  Euclidean distance minimization  $\underset{i}{\mathrm{min}}{\sum}_{t=1}^{24}\left(w{\left(t\right)}^{f}w{\left(t\right)}^{i}\right),i\in \theta $ (1)  θ: historical days f: forecast day i: historical day in θ $w$: weather factor under consideration 
[12,22]  Weighted Euclidean distance minimization  $\underset{t}{\mathrm{min}}\sqrt{{w}_{1}{\left(\Delta {L}_{t}\right)}^{2}+{w}_{2}{\left(\Delta {L}_{s}\right)}^{2}+{w}_{3}{\left(\Delta {T}_{t}\right)}^{2}}$ (2)  $\Delta {L}_{t}$: deviation of load of forecast day and historical day $\Delta {L}_{s}$: deviation of slope between load on forecast day and load of historical day $\Delta {T}_{t}$: deviation of temperature between forecast day and historical day ${w}_{n}$: Weight factor 
Method  Publications  Technique 

SimilarPattern Method  
[12,20,21,22,23,35,36]  similar day  
[24,25,26,27,37,38,39,40,41]  patternsequence  
[28,29,30,31,32,33,34]  sequence learning 
Publication  Technique 

[11,48,49]  Stepwise 
[57,58]  Filter 
[3,9,47,51,65,66]  Correlation 
[47,53,54,55,67]  Mutual Information 
[60,61,62,63,64,68,69]  Optimization Algorithms 
Combination Method  Formulae  Parameters 

Linear Programming  $w=\mathrm{arg}\text{}\underset{\mathrm{w}}{\mathrm{min}}{\displaystyle {\displaystyle \sum}_{t=1}^{T}}\frac{1}{T}\frac{\left\widehat{Y}\tilde{Y}\right}{\widehat{Y}}$ $\tilde{Y}={\displaystyle {\displaystyle \sum}_{n=1}^{N}}{w}_{n}\text{}{\tilde{Y}}_{n}$  $\widehat{Y}$: base forecast $\tilde{Y}$: adjusted forecast $w$: weight factor 
Quadratic Programming  $\underset{\tilde{Y}}{\mathrm{min}}\frac{1}{2}{(\widehat{Y}\tilde{Y})}^{T}{\Sigma}^{1}(\widehat{Y}\tilde{Y})$ $\tilde{Y}={\left[{\tilde{a}}^{T},{\tilde{b}}^{T}\right]}^{T}$ $\tilde{a}=p\text{}\tilde{b}$  $\widehat{Y}$: base forecast $\tilde{Y}$: adjusted forecast $\tilde{a}$: load of the aggregated level $\tilde{b}$: Load of the disaggregated level $p$: participation factor 
Method  Advantages  Disadvantages 

SimilarPattern Method 


Variable Selection Method 


Hierarchical Method 


Weather Station Selection 


© 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
Fallah, S.N.; Ganjkhani, M.; Shamshirband, S.; Chau, K.w. Computational Intelligence on ShortTerm Load Forecasting: A Methodological Overview. Energies 2019, 12, 393. https://doi.org/10.3390/en12030393
Fallah SN, Ganjkhani M, Shamshirband S, Chau Kw. Computational Intelligence on ShortTerm Load Forecasting: A Methodological Overview. Energies. 2019; 12(3):393. https://doi.org/10.3390/en12030393
Chicago/Turabian StyleFallah, Seyedeh Narjes, Mehdi Ganjkhani, Shahaboddin Shamshirband, and Kwokwing Chau. 2019. "Computational Intelligence on ShortTerm Load Forecasting: A Methodological Overview" Energies 12, no. 3: 393. https://doi.org/10.3390/en12030393