Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Systematic Review Article

A Comprehensive Review of Various Machine Learning Techniques used in Load Forecasting

Author(s): Divya Priyadharshini Mohan* and MSP Subathra

Volume 16, Issue 3, 2023

Published on: 21 October, 2022

Page: [197 - 210] Pages: 14

DOI: 10.2174/2352096515666220930144336

Price: $65

Abstract

Background: Load forecasting is a crucial element in power utility business load forecasting and has influenced key decision-makers in the industry to predict future energy demand with a low error percentage to supply consumers with load-shedding-free and uninterruptible power. By applying the right technique, utility companies may save millions of dollars by using load prediction with a lower proportion of inaccuracy.

Aims: This study paper aims to analyse the recently published papers (using the New York Independent System Operator's database) on load forecasting and find the most optimised forecasting method for electric load forecasting.

Methods: An overview of existing electric load forecasting technology with a complete examination of multiple load forecasting models and an in-depth analysis of their MAPE benefits, challenges, and influencing factors is presented. The paper reviews hybrid models created by combining two or more predictive models, each offering better performance due to their algorithm's merits. Hybrid models outperform other machine learning (ML) approaches in accurately forecasting power demand.

Results: Through the study, it is understood that hybrid methods show promising features. Deep learning algorithms were also studied for long-term forecasting.

Conclusion: In the future, we can extend the study by extensively studying deep learning methods.

Keywords: Artificial neural networks, deep learning, load forecasting, machine learning, computational modeling, NYISO dataset

« Previous Next »
Graphical Abstract

[1]
S. Aasim, S.N. Singh, and A. Mohapatra, "Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model", Appl. Soft Comput., vol. 111, p. 107730, 2021.
[http://dx.doi.org/10.1016/j.asoc.2021.107730]
[2]
R. Ahmadiahangar, T. Häring, A. Rosin, T. Korõtko, and J. Martins, "Residential load forecasting for flexibility prediction using machine learning-based regression model", 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), Jun 10-14, 2019, Genova, Italy, pp. 1-4, 2019.
[http://dx.doi.org/10.1109/EEEIC.2019.8783634]
[3]
M. Alamaniotis, S. Chatzidakis, and L.H. Tsoukalas, "Monthly load forecasting using kernel-based Gaussian Process Regression", Med. Power, vol. 60, p. 8, 2014.
[http://dx.doi.org/10.1049/cp.2014.1693]
[4]
A. Almalaq, and G. Edwards, "A review of deep learning methods applied on load forecasting", 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Dec 18-21, 2017, Cancun, Mexico , pp. 511-516, 2017.
[http://dx.doi.org/10.1109/ICMLA.2017.0-110]
[5]
M.K. Azad, S. Uddin, and M. Takruri, "Support vector regression-based electricity peak load forecasting", 2018 11th International Symposium on Mechatronics and Its Applications (ISMA),Mar 04-06, 2018 , Sharjah, United Arab Emirates, pp. 1-5, 2018.
[http://dx.doi.org/10.1109/ISMA.2018.8330143]
[6]
M. Baghel, A. Ghosh, N.K. Singh, and A.K. Singh, "Short—Term electric load forecasting using SVR implementing LibSVM package and Python code", 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), Dec 09-11, 2016, Varanasi, India, pp. 485-489, 2016.
[http://dx.doi.org/10.1109/UPCON.2016.7894702]
[7]
E. Bećirović, and M. Ćosović, "Machine learning techniques for short-term load forecasting", 2016 4th International Symposium on Environmental Friendly Energies and Applications (EFEA), Sept 14-16, 2016, Belgrade, Serbia, pp. 1-4, 2016.
[http://dx.doi.org/10.1109/EFEA.2016.7748789]
[8]
H.G. Bergsteinsson, J.K. Møller, P. Nystrup, Ó.P. Pálsson, D. Guericke, and H. Madsen, "Heat load forecasting using adaptive temporal hierarchies", Appl. Energy, vol. 292, p. 116872, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2021.116872]
[9]
M. Bessani, J.A.D. Massignan, T.M.O. Santos, J.B.A. London Jr, and C.D. Maciel, "Multiple households very short-term load forecasting using bayesian networks", Electr. Power Syst. Res., vol. 189, p. 106733, 2020.
[http://dx.doi.org/10.1016/j.epsr.2020.106733]
[10]
H. Bian, Y. Zhong, J. Sun, and F. Shi, "Study on power consumption load forecast based on K-means clustering and FCM–BP model", Energy Rep., vol. 6, pp. 693-700, 2020.
[http://dx.doi.org/10.1016/j.egyr.2020.11.148]
[11]
S. Bouktif, A. Fiaz, A. Ouni, and M. Serhani, "Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches", Energies, vol. 11, no. 7, p. 1636, 2018.
[http://dx.doi.org/10.3390/en11071636]
[12]
V. Bui, V.H. Nguyen, T.L. Pham, J. Kim, and Y.M. Jang, "RNN-based deep learning for one-hour ahead load forecasting", 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Feb 19-21, 2020, Fukuoka, Japan, pp. 587-589, 2020.
[http://dx.doi.org/10.1109/ICAIIC48513.2020.9065071]
[13]
L. Buzna, P. De Falco, G. Ferruzzi, S. Khormali, D. Proto, N. Refa, M. Straka, and G. van der Poel, "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations", Appl. Energy, vol. 283, p. 116337, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2020.116337]
[14]
Y. Cai, Q. Xie, C. Wang, and F. Lü, "Short-term load forecasting for city holidays based on genetic support vector machines", 2011 International Conference on Electrical and Control Engineering, Sept 16-18, 2011, Yichang, China, pp. 3144-3147, 2011.
[http://dx.doi.org/10.1109/ICECENG.2011.6057627]
[15]
A.C. Caminero, S. Ros, R. Hern’ndez, A. Robles-Gomez, and R. Pastor, "Load forecasting mechanism for e-learning infrastructures using exponential smoothing", 2011 IEEE 11th International Conference on Advanced Learning Technologies, Jul 06-08, 2011, Athens, GA, USA, pp. 364-365, 2011.
[http://dx.doi.org/10.1109/ICALT.2011.114]
[16]
E.M. Carreno, A. Padilha-Feltrin, and A.G. Leal, "Spatial electric load forecasting using an evolutionary heuristic", Sba: Controle & Automação Sociedade Brasileira de Automatica, vol. 21, no. 4, pp. 379-388, 2010.
[17]
P.P.K. Chan, W.C. Chen, W.W.Y. Ng, and D.S. Yeung, "Multiple classifier system for short term load forecast of Microgrid", 2011 Inter-national Conference on Machine Learning and Cybernetics, Jul 10-13, 2011, Guilin, China, pp. 1268-1273, 2011.
[http://dx.doi.org/10.1109/ICMLC.2011.6016936]
[18]
C. Chang-chun, and W. Min, "Support vector machines with similar day’s training sample application in short-term load forecasting", 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, Apr, 6-9, 2008, Nanjing, pp. 1221-1225, 2008.
[http://dx.doi.org/10.1109/DRPT.2008.4523593]
[19]
Q. Chen, M. Xia, T. Lu, X. Jiang, W. Liu, and Q. Sun, "Short-term load forecasting based on deep learning for end-user transformer sub-ject to volatile electric heating loads", IEEE Access, vol. 7, pp. 162697-162707, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2949726]
[20]
Q. Chen, W. Zhang, K. Zhu, D. Zhou, H. Dai, and Q. Wu, "A novel trilinear deep residual network with self-adaptive Dropout method for short-term load forecasting", Expert Syst. Appl., vol. 182, p. 115272, 2021.
[http://dx.doi.org/10.1016/j.eswa.2021.115272]
[21]
Y. Chen, and D. Zhang, "Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory", Adv. Appl. Ener., vol. 1, p. 100004, 2021.
[http://dx.doi.org/10.1016/j.adapen.2020.100004]
[22]
Z. Chen, Y. Chen, T. Xiao, H. Wang, and P. Hou, "A novel short-term load forecasting framework based on time-series clustering and early classification algorithm", Energy Build., vol. 251, p. 111375, 2021.
[http://dx.doi.org/10.1016/j.enbuild.2021.111375]
[23]
Q. Cheng, J. Yao, H. Wu, S. Chen, C. Liu, and P. Yao, "Short-term load forecasting with weather component based on improved extreme learning machine", 2013 Chinese Automation Congress, Nov, 07-08, 2013, Changsha, China, pp. 316-321, 2013.
[http://dx.doi.org/10.1109/CAC.2013.6775750]
[24]
M. Cui, J. Wang, and M. Yue, "Machine learning-based anomaly detection for load forecasting under cyberattacks", IEEE Trans. Smart Grid, vol. 10, no. 5, pp. 5724-5734, 2019.
[http://dx.doi.org/10.1109/TSG.2018.2890809]
[25]
M.A. da Silva, T. Abreu, C.R. Santos-Júnior, and C.R. Minussi, "Load forecasting for smart grid based on continuous-learning neural network", Electr. Power Syst. Res., vol. 201, p. 107545, 2021.
[http://dx.doi.org/10.1016/j.epsr.2021.107545]
[26]
K. Dab, K. Agbossou, N. Henao, Y. Dube, S. Kelouwani, and S.S. Hosseini, "A compositional kernel-based gaussian process approach to day-ahead residential load forecasting", Energy Build., vol. 254, p. 111459, 2021.
[http://dx.doi.org/10.1016/j.enbuild.2021.111459]
[27]
M. Dabbaghjamanesh, A. Moeini, and A. Kavousi-Fard, "Reinforcement learning-based load forecasting of electric vehicle charging station using Q -learning technique", IEEE Trans. Industr. Inform., vol. 17, no. 6, pp. 4229-4237, 2021.
[http://dx.doi.org/10.1109/TII.2020.2990397]
[28]
W. Dinq, F-G. Dong, and S-D. Yang, "Load forecasting based on clustering analysis using fuzzy logic", 2005 International Conference on Machine Learning and Cybernetics, Aug 18-21, 2005, vol. 5, Guangzhou, China, pp. 2640-2645, 2005.
[http://dx.doi.org/10.1109/ICMLC.2005.1527390]
[29]
Y. Dong, Z. Dong, T. Zhao, Z. Li, and Z. Ding, "Short term load forecasting with markovian switching distributed deep belief networks", Int. J. Electr. Power Energy Syst., vol. 130, p. 106942, 2021.
[http://dx.doi.org/10.1016/j.ijepes.2021.106942]
[30]
H. Eskandari, M. Imani, and M.P. Moghaddam, "Convolutional and recurrent neural network based model for short-term load forecasting", Electr. Power Syst. Res., vol. 195, p. 107173, 2021.
[http://dx.doi.org/10.1016/j.epsr.2021.107173]
[31]
M. Espinoza, J. Suykens, R. Belmans, and B.D. Moor, "Electric load forecasting", IEEE Control Syst., vol. 27, no. 5, pp. 43-57, 2007.
[http://dx.doi.org/10.1109/MCS.2007.904656]
[32]
G.F. Fan, M. Yu, S.Q. Dong, Y.H. Yeh, and W.C. Hong, "Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling", Util. Policy, vol. 73, p. 101294, 2021.
[http://dx.doi.org/10.1016/j.jup.2021.101294]
[33]
J. Faraji, A. Ketabi, H. Hashemi-Dezaki, M. Shafie-Khah, and J.P.S. Catalão, "Optimal day-ahead self-scheduling and operation of prosumer microgrids using hybrid machine learning-based weather and load forecasting", IEEE Access, vol. 8, pp. 157284-157305, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3019562]
[34]
B. Farsi, M. Amayri, N. Bouguila, and U. Eicker, "On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach", IEEE Access, vol. 9, pp. 31191-31212, 2021.
[http://dx.doi.org/10.1109/ACCESS.2021.3060290]
[35]
C. Feng, M. Sun, and J. Zhang, "Reinforced deterministic and probabilistic load forecasting via $Q$ -learning dynamic model selection", IEEE Trans. Smart Grid, vol. 11, no. 2, pp. 1377-1386, 2020.
[http://dx.doi.org/10.1109/TSG.2019.2937338]
[36]
C. Feng, and J. Zhang, "Hourly-similarity based solar forecasting using multi-model machine learning blending", 2018 IEEE Power Energy Society General Meeting (PESGM), Aug 5-10, 2018, Portland, OR, USA, pp. 1-5, 2018.
[http://dx.doi.org/10.1109/PESGM.2018.8586091]
[37]
J. Guo, P. Zhang, D. Wu, Z. Liu, X. Liu, S. Zhang, X. Yang, and H. Ge, "Multi-objective optimization design and multi-attribute decision-making method of a distributed energy system based on nearly zero-energy community load forecasting", Energy, vol. 122124. 2021.
[http://dx.doi.org/10.1016/j.energy.2021.122124]
[38]
W. Guo, L. Che, M. Shahidehpour, and X. Wan, "Machine-Learning based methods in short-term load forecasting", Electr. J., vol. 34, no. 1, p. 106884, 2021.
[http://dx.doi.org/10.1016/j.tej.2020.106884]
[39]
X. Guo, Y. Gao, Y. Li, D. Zheng, and D. Shan, "Short-term household load forecasting based on long- and short-term time-series network", Energy Rep., vol. 7, pp. 58-64, 2021.
[http://dx.doi.org/10.1016/j.egyr.2021.02.023]
[40]
X. Guo, Y. Liang, C. Wu, and H. Wang, "Electric load forecasting using SVMS", 2006 International Conference on Machine Learning and Cybernetics, Aug 13-16, 2006, Dalian, China, pp. 4213-4215, 2006.
[http://dx.doi.org/10.1109/ICMLC.2006.258945]
[41]
X-C. Guo, Z-Y. Chen, H-W. Ge, and Y-C. Liang, "Short-term load forecasting using neural network with principal component analysis", Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), Aug 26-29, 2004, vol. 6, Shanghai, China, pp. 3365-3369, 2004.
[http://dx.doi.org/10.1109/ICMLC.2004.1380362]
[42]
Y. Guo, D. Niu, and Y. Chen, "Support vector machine model in electricity load forecasting", 2006 International Conference on Machine Learning and Cybernetics, Aug 13-16, 2006, Dalian, China, pp. 2892-2896, 2006.
[http://dx.doi.org/10.1109/ICMLC.2006.259076]
[43]
A. Gupta, and A. Kumar, "Mid term daily load forecasting using ARIMA, wavelet-ARIMA and machine learning", 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), Jun 9-12, 2020, Madrid, Spain, pp. 1-5, 2020.
[http://dx.doi.org/10.1109/EEEIC/ICPSEurope49358.2020.9160563]
[44]
S. Haben, S. Arora, G. Giasemidis, M. Voss, and D. Vukadinović Greetham, "Review of low voltage load forecasting: Methods, applications, and recommendations", Appl. Energy, vol. 304, p. 117798, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2021.117798]
[45]
G. Hafeez, I. Khan, S. Jan, I.A. Shah, F.A. Khan, and A. Derhab, "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid", Appl. Energy, vol. 299, p. 117178, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2021.117178]
[46]
M.A. Hammad, B. Jereb, B. Rosi, and D. Dragan, "Methods and models for electric load forecasting: A comprehensive review", Logis. Sustain. Trans., vol. 11, no. 1, pp. 51-76, 2020.
[http://dx.doi.org/10.2478/jlst-2020-0004]
[47]
T. Häring, R. Ahmadiahangar, A. Rosin, T. Korõtko, and H. Biechl, "Accuracy analysis of selected time series and machine learning methods for smart cities based on estonian electricity consumption forecast", 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERING), Jul 8-10, 2020, vol. 1, Setubal, Portugal, pp. 425-428, 2020.
[http://dx.doi.org/10.1109/CPE-POWERENG48600.2020.9161690]
[48]
H. He, J. Pan, N. Lu, B. Chen, and R. Jiao, "Short-term load probabilistic forecasting based on quantile regression convolutional neural network and Epanechnikov kernel density estimation", Energy Rep., vol. 6, pp. 1550-1556, 2020.
[http://dx.doi.org/10.1016/j.egyr.2020.10.053]
[49]
Y. He, J. Deng, and H. Li, "Short-term power load forecasting with deep belief network and copula models", s 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Aug 26-27, 2017, vol. 1, Hangzhou, China, pp. 191-194, 2017.
[http://dx.doi.org/10.1109/IHMSC.2017.50]
[50]
Y.J. He, Y.C. Zhu, J.C. Gu, and C.Q. Yin, "Similar day selecting based neural network model and its application in short-term load forecasting", 2005 International Conference on Machine Learning and Cybernetics, vol. 8, Aug 18-21, 2005, Guangzhou, China, pp. 4760-4763, 2005.
[http://dx.doi.org/10.1109/ICMLC.2005.1527779]
[51]
Y. Huang, N. Hasan, C. Deng, and Y. Bao, "Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting", Energy, vol. 122245. 2021.
[http://dx.doi.org/10.1016/j.energy.2021.122245]
[52]
Y.S. Huang, J.J. Deng, and Y.Y. Zhang, "Application of SVM based on immune genetic fuzzy clustering algorithm to short-term load forecasting", 2008 International Conference on Machine Learning and Cybernetics, Jul 12-15, 2008, Kunming, China, pp. 2646-2650, 2008.
[http://dx.doi.org/10.1109/ICMLC.2008.4620855]
[53]
Z. Huang, H. Guo, P. Yang, and Z. Xu, "Electricity load forecasting method for large consumers", 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia), Jun 03-07, 2017, Kaohsiung, Taiwan, pp. 1703-1707, 2017.
[http://dx.doi.org/10.1109/IFEEC.2017.7992304]
[54]
M. Imani, "Electrical load-temperature CNN for residential load forecasting", Energy, vol. 227, p. 120480, 2021.
[http://dx.doi.org/10.1016/j.energy.2021.120480]
[55]
A.S. Jaber, K.A. Satar, and N.A. Shalash, "Short term load forecasting for electrical dispatcher of Baghdad city based on SVM-PSO method", 2018 2nd International Conference on Electrical Engineering and Informatics (ICon EEI), Oct 16-17, 2018, Batam, Indonesia, pp. 140-143, 2018.
[http://dx.doi.org/10.1109/ICon-EEI.2018.8784316]
[56]
M. Jawad, M.S.A. Nadeem, S.O. Shim, I.R. Khan, A. Shaheen, N. Habib, L. Hussain, and W. Aziz, "Machine learning based cost effective electricity load forecasting model using correlated meteorological parameters", IEEE Access, vol. 8, pp. 146847-146864, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3014086]
[57]
Q. Jiang, J.X. Zhu, M. Li, and H.Y. Qing, "Electricity power load forecast via long short-term memory recurrent neural networks", 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC), Apr 19-21, 2018, Wuhan, China, pp. 265-268, 2018.
[http://dx.doi.org/10.1109/ICNISC.2018.00060]
[58]
S. Jiang, H. Li, Y. Wang, Z. Yang, X. Zhu, W. Liu, and J. Han, "Short-term load forecasting method for AC/DC distribution system based on ensemble learning", 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC), Sept 07-09, 2019, Beijing, China, pp. 1826-1830, 2019.
[http://dx.doi.org/10.1109/CIEEC47146.2019.CIEEC-2019627]
[59]
J. Jiao, Z. Tang, P. Zhang, M. Yue, and J. Yan, "Cyberattack-resilient load forecasting with adaptive robust regression", Int. J. Forecast., 2021.
[http://dx.doi.org/10.1016/j.ijforecast.2021.06.009]
[60]
H. Alfares, and N. Mohammad, "Electric load forecasting: Literature survey and classification of methods", Int. J. Sys. Sci., vol. 33, no. 1, pp. 23-34, 2002.
[61]
M. Khashei, and F. Chahkoutahi, "A comprehensive low-risk and cost parallel hybrid method for electricity load forecasting", Comput. Ind. Eng., vol. 155, p. 107182, 2021.
[http://dx.doi.org/10.1016/j.cie.2021.107182]
[62]
A.S. Khwaja, A. Anpalagan, M. Naeem, and B. Venkatesh, "Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting", Electr. Power Syst. Res., vol. 179, p. 106080, 2020.
[http://dx.doi.org/10.1016/j.epsr.2019.106080]
[63]
W. Kong, Z.Y. Dong, F. Luo, K. Meng, W. Zhang, F. Wang, and X. Zhao, "Effect of automatic hyperparameter tuning for residential load forecasting via deep learning", 2017 Australasian Universities Power Engineering Conference (AUPEC), Nov 19-22, 2017, Melbourne, VIC, Australia, pp. 1-6, 2017.
[http://dx.doi.org/10.1109/AUPEC.2017.8282478]
[64]
C. Kuster, Y. Rezgui, and M. Mourshed, "Electrical load forecasting models: A critical systematic review", Sustain Cities Soc., vol. 35, pp. 257-270, 2017.
[http://dx.doi.org/10.1016/j.scs.2017.08.009]
[65]
A.V. Kychkin, and G.C. Chasparis, "Feature and model selection for day-ahead electricity-load forecasting in residential buildings", Energy Build., vol. 249, p. 111200, 2021.
[http://dx.doi.org/10.1016/j.enbuild.2021.111200]
[66]
J. Lemos-Vinasco, P. Bacher, and J.K. Møller, "Probabilistic load forecasting considering temporal correlation: Online models for the prediction of households’ electrical load", Appl. Energy, vol. 303, p. 117594, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2021.117594]
[67]
C. Li, "A fuzzy theory-based machine learning method for workdays and weekends short-term load forecasting", Energy Build., vol. 245, p. 111072, 2021.
[http://dx.doi.org/10.1016/j.enbuild.2021.111072]
[68]
F. Li, H. Zheng, X. Li, and F. Yang, "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model", Appl. Energy, vol. 303, p. 117623, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2021.117623]
[69]
L. Li, C.J. Meinrenken, V. Modi, and P.J. Culligan, "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features", Appl. Energy, vol. 287, p. 116509, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2021.116509]
[70]
Y. Li, Z. Lv, N. Pang, Y. Liu, G. Zhao, and J. Hu, "Identifying working day and rest day data based on machine learning method for more accurate transformer load forecasting", 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Sept 06-10, 2020, Beijing, China, pp. 1-4, 2020.
[http://dx.doi.org/10.1109/ICHVE49031.2020.9279621]
[71]
Y. Li, S. Zhang, R. Hu, and N. Lu, "A meta-learning based distribution system load forecasting model selection framework", Appl. Energy, vol. 294, p. 116991, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2021.116991]
[72]
Z. Li, Y. Qin, S. Hou, R. Zhang, and H. Sun, "Renewable energy system based on IFOA-BP neural network load forecast", Energy Rep., vol. 6, pp. 1585-1590, 2020.
[http://dx.doi.org/10.1016/j.egyr.2020.10.048]
[73]
Z. Liao, H. Pan, X. Huang, R. Mo, X. Fan, H. Chen, L. Liu, and Y. Li, "Short-term load forecasting with dense average network", Expert Syst. Appl., vol. 186, p. 115748, 2021.
[http://dx.doi.org/10.1016/j.eswa.2021.115748]
[74]
M. López, C. Sans, and S. Valero, "Automatic classification of special days for short-term load forecasting", Electr. Power Syst. Res., vol. 202, p. 107533, 2022.
[http://dx.doi.org/10.1016/j.epsr.2021.107533]
[75]
N. Lu, J. Zhou, Y. He, and Y. Liu, "Simulated annealing theory-based particle swarm optimization for support vector machine model in short-term load forecasting", 2009 International Conference on Information Engineering and Computer Science, pp. 1-4, 2009.
[http://dx.doi.org/10.1109/ICIECS.2009.5366400]
[76]
A.A. Mamun, M. Sohel, N. Mohammad, M.S. Haque Sunny, D.R. Dipta, and E. Hossain, "A comprehensive review of the load forecasting techniques using single and hybrid predictive models", IEEE Access, vol. 8, pp. 134911-134939, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3010702]
[77]
L. Marwala, and B. Twala, "Electricity load forecasting using an ensemble of optimally-pruned and basic extreme learning machines", 2017 9th IEEE-GCC Conference and Exhibition (GCCCE), May 08-11, 2017, Manama, Bahrain, pp. 1-6, 2017.
[http://dx.doi.org/10.1109/IEEEGCC.2017.8448125]
[78]
M. Medykovskvi, O. Pavliuk, and R. Sydorenko, "Use of machine learning technologies for the electric consumption forecast", 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Sept 11-14, 2018,, vol. 1, Lviv, Ukraine, pp. 432-435, 2018.
[http://dx.doi.org/10.1109/STC-CSIT.2018.8526617]
[79]
H. Mori, and E. Kurata, "An efficient kernel machine technique for short-term load forecasting under smart grid environment", 2012 IEEE Power and Energy Society General Meeting, Jul, 22-26, 2012,, San Diego, CA, USA, pp. 1-4, 2012.
[http://dx.doi.org/10.1109/PESGM.2012.6345687]
[80]
S. Motepe, A.N. Hasan, and R. Stopforth, "Improving load forecasting process for a power distribution network using hybrid AI and deep learning algorithms", IEEE Access, vol. 7, pp. 82584-82598, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2923796]
[81]
A. Naz, N. Javaid, and S. Javaid, "Enhanced recurrent extreme learning machine using gray wolf optimization for load forecasting", 2018 IEEE 21st International Multi-Topic Conference (INMIC), Nov, 1-2, 2018, Karachi, Pakistan, pp. 1-5, 2018.
[http://dx.doi.org/10.1109/INMIC.2018.8595654]
[82]
A. Naz, N. Javaid, A. Khalid, M. Shoaib, and M. Imran, "Electric load forecasting using EEMD and machine learning techniques", 2020 International Wireless Communications and Mobile Computing (IWCMC), Jun, 15-19, 2020, Limassol, Cyprus, pp. 2124-2127, 2020.
[http://dx.doi.org/10.1109/IWCMC48107.2020.9148094]
[83]
D.X. Niu, C.X. Li, M. Meng, and W. Shang, "Short-term load forecasting using a CBR-ANN model", 2007 International Conference on Machine Learning and Cybernetics, Aug, 19-22, 2007, Hong Kong, China, pp. 2719-2723, 2007.
[http://dx.doi.org/10.1109/ICMLC.2007.4370609]
[84]
D.X. Niu, W. Li, L.M. Cheng, and X.H. Gu, "Mid-term load forecasting based on dynamic least squares SVMS", 2008 International Conference on Machine Learning and Cybernetics, Jul, 12-15, 2008, Kunming, pp. 800-804, 2008.
[http://dx.doi.org/10.1109/ICMLC.2008.4620513]
[85]
D-X. Niu, Q. Wang, and J-C. Li, "Research on support vector machine model based on grey relation analysis for daily load forecasting", 2005 International Conference on Machine Learning and Cybernetics, Aug 18-21, 2005, vol. 7, Guangzhou, China, pp. 4254-4259, 2005.
[http://dx.doi.org/10.1109/ICMLC.2005.1527684]
[86]
D-X. Niu, Q. Wanq, and J-C. Li, "Short term load forecasting model using support vector machine based on artificial neural network", 2005 International Conference on Machine Learning and Cybernetics, Aug 18-21, 2005, vol. 7, Guangzhou, China, pp. 4260-4265, 2005.
[http://dx.doi.org/10.1109/ICMLC.2005.1527685]
[87]
L. Niu, J. Zhao, and M. Liu, "Application of relevance vector regression model based on sparse Bayesian learning to long-term electricity demand forecasting", 2009 International Conference on Mechatronics and Automation, Aug 09-12, 2009, Changchun, China, pp. 2363-2367, 2009.
[http://dx.doi.org/10.1109/ICMA.2009.5246213]
[88]
B.N. Oreshkin, G. Dudek, P. Pełka, and E. Turkina, "N-BEATS neural network for mid-term electricity load forecasting", Appl. Energy, vol. 293, p. 116918, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2021.116918]
[89]
V. Papadopoulos, T. Delerue, J. Van Ryckeghem, and J. Desmet, "Assessing the impact of load forecasting accuracy on battery dispatching strategies with respect to Peak Shaving and Time-of-Use (TOU) applications for industrial consumers", 2017 52nd International Universities Power Engineering Conference (UPEC), Aug 28-31, 2017, Heraklion, Greece, pp. 1-5, 2017.
[http://dx.doi.org/10.1109/UPEC.2017.8231939]
[90]
B. Peng, C. Wang, and X. Tang, "Research on power system load forecasting model based on machine learning", 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Apr, 28-29, 2019, Qiqihar, China, pp. 477-480, 2019.
[http://dx.doi.org/10.1109/ICMTMA.2019.00111]
[91]
P. Popovski, M. Kostov, M. Atanasovski, and G. Veljanovski, "Power load forecast for north macedonia using machine learning", 2020 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Sept 10-12, 2020, Niš, Serbia, pp. 106-109, 2020.
[http://dx.doi.org/10.1109/ICEST49890.2020.9232837]
[92]
P. Prashanthi, and K. Priyadarsini, "A comparative study of the performance of machine learning based load forecasting methods", 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Mar 25-27, 2021, Coimbatore, India, pp. 132-136, 2021.
[http://dx.doi.org/10.1109/ICAIS50930.2021.9395834]
[93]
J. Qin, Y. Zhang, S. Fan, X. Hu, Y. Huang, Z. Lu, and Y. Liu, "Multi-task short-term reactive and active load forecasting method based on attention-LSTM model", Int. J. Electr. Power Energy Syst., vol. 135, p. 107517, 2022.
[http://dx.doi.org/10.1016/j.ijepes.2021.107517]
[94]
R. Rajabi, and A. Estebsari, "Deep learning-based forecasting of individual residential loads using recurrence plots", IEEE Milan Power Tech, Jun 23-27, 2019, Milan, Italy, pp. 1-5, 2019.
[http://dx.doi.org/10.1109/PTC.2019.8810899]
[95]
H. Sangrody, N. Zhou, S. Tutun, B. Khorramdel, M. Motalleb, and M. Sarailoo, "Long term forecasting using machine learning methods", 2018 IEEE Power and Energy Conference at Illinois (PECI), Feb 22-23, 2018, Champaign, IL, USA, pp. 1-5, 2018.
[http://dx.doi.org/10.1109/PECI.2018.8334980]
[96]
N. Shabbir, R. Amadiahangar, H.A. Raja, L. Kütt, and A. Rosin, "Residential load forecasting using recurrent neural networks", 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERING), Jul 08-10, 2020, vol. 1, Setubal, Portugal, pp. 478-481, 2020.
[http://dx.doi.org/10.1109/CPE-POWERENG48600.2020.9161565]
[97]
N. Singh, C. Vyjayanthi, and C. Modi, "Multi-step short-term electric load forecasting using 2D convolutional neural networks", IEEEHYDCON, Sept 11-12, 2020, Hyderabad, India, pp. 1-5, 2020.
[http://dx.doi.org/10.1109/HYDCON48903.2020.9242917]
[98]
S.M. Sulaiman, P. Aruna Jeyanthy, D. Devaraj, S.S. Mohammed, and K.V. Shihabudheen, "Smart meter data analytics for load prediction using extreme learning machines and artificial neural networks", 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCHES), Dec 18-20, 2019, Krishnankoil, India, pp. 1-4, 2019.
[http://dx.doi.org/10.1109/INCCES47820.2019.9167736]
[99]
S. Ungureanu, V. Ţopa, and A. Cziker, "Industrial load forecasting using machine learning in the context of smart grid", 2019 54th International Universities Power Engineering Conference (UPEC), Sept 03-06, 2019, Bucharest, Romania, pp. 1-6, 2019.
[http://dx.doi.org/10.1109/UPEC.2019.8893540]
[100]
T. Walser, and A. Sauer, "Typical load profile-supported convolutional neural network for short-term load forecasting in the industrial sector", Energy and AI, vol. 5, p. 100104, 2021.
[http://dx.doi.org/10.1016/j.egyai.2021.100104]
[101]
J. Wang, Z. Liu, and P. Lu, "Electricity load forecasting based on adaptive quantum-behaved particle swarm optimization and support vector machines on global level", 2008 International Symposium on Computational Intelligence and Design, Oct 17-18, 2008, Wuhan, China, pp. 233-236, 2008.
[http://dx.doi.org/10.1109/ISCID.2008.31]
[102]
S. Wang, X. Deng, H. Chen, Q. Shi, and D. Xu, "A bottom-up short-term residential load forecasting approach based on appliance characteristic analysis and multi-task learning", Electr. Power Syst. Res., vol. 196, p. 107233, 2021.
[http://dx.doi.org/10.1016/j.epsr.2021.107233]
[103]
S. Wang, G. Xue, C. Ping, D. Wang, F. You, and T. Jiang, "The application of forecasting algorithms on electric vehicle power load", 2018 IEEE International Conference on Mechatronics and Automation (ICMA), Aug 05-08, 2018, Changchun, China, pp. 1371-1375, 2018.
[http://dx.doi.org/10.1109/ICMA.2018.8484313]
[104]
T. Wang, C.S. Lai, W.W.Y. Ng, K. Pan, M. Zhang, A. Vaccaro, and L.L. Lai, "Deep autoencoder with localized stochastic sensitivity for short-term load forecasting", Int. J. Electr. Power Energy Syst., vol. 130, p. 106954, 2021.
[http://dx.doi.org/10.1016/j.ijepes.2021.106954]
[105]
Y. Wang, K. Ma, X. Li, Y. Liang, Y. Hu, J. Li, and H. Liu, "Multi-type load forecasting of IES based on load correlation and stacked auto-encode extreme learning machine", 2020 10th International Conference on Power and Energy Systems (ICPES), Dec 25-27, 2020, Chengdu, China, pp. 585-589, 2020.
[http://dx.doi.org/10.1109/ICPES51309.2020.9349738]
[106]
Y. Wang, S. Sun, X. Chen, X. Zeng, Y. Kong, J. Chen, Y. Guo, and T. Wang, "Short-term load forecasting of industrial customers based on SVMD and XGBoost", Int. J. Electr. Power Energy Syst., vol. 129, p. 106830, 2021.
[http://dx.doi.org/10.1016/j.ijepes.2021.106830]
[107]
Z. Wang, X. Zhou, J. Tian, and T. Huang, "Hierarchical parameter optimization based support vector regression for power load forecasting", Sustain Cities Soc., vol. 71, p. 102937, 2021.
[http://dx.doi.org/10.1016/j.scs.2021.102937]
[108]
K.P. Warrior, M. Shrenik, and N. Soni, "Short-term electrical load forecasting using predictive machine learning models", 2016 IEEE Annual India Conference (INDICON), Dec 16-18, 2016, Bangalore, India, pp. 1-6, 2016.
[http://dx.doi.org/10.1109/INDICON.2016.7839103]
[109]
J. Wu, Y.G. Wang, Y.C. Tian, K. Burrage, and T. Cao, "Support vector regression with asymmetric loss for optimal electric load forecasting", Energy, vol. 223, p. 119969, 2021.
[http://dx.doi.org/10.1016/j.energy.2021.119969]
[110]
X. Wu, C. Dou, and D. Yue, "Electricity load forecast considering search engine indices", Electr. Power Syst. Res., vol. 199, p. 107398, 2021.
[http://dx.doi.org/10.1016/j.epsr.2021.107398]
[111]
H. Xu, J.H. Wang, and S.Q. Zheng, "Online daily load forecasting based on support vector machines", 2005 International Conference on Machine Learning and Cybernetics, Aug 18-21, 2005, vol. 7, Guangzhou, China, pp. 3985-3990, 2005.
[http://dx.doi.org/10.1109/ICMLC.2005.1527634]
[112]
X. Xu, Y. Chen, Y. Goude, and Q. Yao, "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression", Appl. Energy, vol. 301, p. 117465, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2021.117465]
[113]
J. Yang, and Q. Wang, "A deep learning load forecasting method based on load type recognition", 2018 International Conference on Machine Learning and Cybernetics (ICMLC), Jul 15-18, 2018, Chengdu, China, pp. 173-177, 2018.
[http://dx.doi.org/10.1109/ICMLC.2018.8527022]
[114]
K. Yang, G. Shan, and L. Zhao, "Correlation coefficient method for support vector machine input samples", 2006 International Conference on Machine Learning and Cybernetics, Aug 13-16, 2006, Dalian, China, pp. 2857-2861, 2006.
[http://dx.doi.org/10.1109/ICMLC.2006.259069]
[115]
FQTSFM, "A fuzzy-quantum time series forecasting model", Inf. Sci., vol. 556, pp. 57-79, 2021.
[116]
P. Singh, "A novel hybrid time series forecasting model based on neutrosophic-PSO approach", Int. J. Mach. Learn. Cybern., vol. 11, no. 8, pp. 1643-1658, 2020.
[http://dx.doi.org/10.1007/s13042-020-01064-z]
[117]
P. Singh, and Y.P. Huang, "A high-order neutrosophic-neurogradient descent algorithm-based expert system for time series forecasting", Int. J. Fuzzy Syst., vol. 21, no. 7, pp. 2245-2257, 2019.
[http://dx.doi.org/10.1007/s40815-019-00690-2]
[118]
P. Singh, and Y.P. Huang, "A new hybrid time series forecasting model based on the neutrosophic set and quantum optimization algorithm", Comput. Ind., vol. 111, pp. 121-139, 2019.
[http://dx.doi.org/10.1016/j.compind.2019.06.004]
[119]
X. Yang, "Daily load forecasting based on rough sets and relevance vector machine", Proceedings of 2011 International Conference on Electronic Mechanical Engineering and Information Technology, Aug 12-14, 2011, Harbin, China, pp. 2946-2949, 2011.
[http://dx.doi.org/10.1109/EMEIT.2011.6023665]
[120]
B. Yildiz, J.I. Bilbao, and A.B. Sproul, "A review and analysis of regression and machine learning models on commercial building electricity load forecasting", Renew. Sustain. Energy Rev., vol. 73, pp. 1104-1122, 2017.
[http://dx.doi.org/10.1016/j.rser.2017.02.023]
[121]
L. Yin, and J. Xie, "Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems", Appl. Energy, vol. 283, p. 116328, 2021.
[http://dx.doi.org/10.1016/j.apenergy.2020.116328]
[122]
H. Zang, R. Xu, L. Cheng, T. Ding, L. Liu, Z. Wei, and G. Sun, "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling", Energy, vol. 229, p. 120682, 2021.
[http://dx.doi.org/10.1016/j.energy.2021.120682]
[123]
G. Zhang, X. Bai, and Y. Wang, "Short-time multi-energy load forecasting method based on CNN-Seq2Seq model with attention mechanism", Mach. Learn. App., vol. 5, p. 100064, 2021.
[http://dx.doi.org/10.1016/j.mlwa.2021.100064]
[124]
L. Zhang, M. Alahmad, and J. Wen, "Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study", Energy Build., vol. 231, p. 110592, 2021.
[http://dx.doi.org/10.1016/j.enbuild.2020.110592]
[125]
N. Zhang, Z. Li, X. Zou, and S.M. Quiring, "Comparison of three short-term load forecast models in Southern California", Energy, vol. 189, p. 116358, 2019.
[http://dx.doi.org/10.1016/j.energy.2019.116358]
[126]
W. Zhang, J. Che, J. Wang, and J. Liang, "Short-term load forecasting by integration of phase space reconstruction, support vector regression and parameter tuning system", 2008 International Seminar on Future Information Technology and Management Engineering, Nov 20, 2008, Leicestershire, UK, pp. 402-405, 2008.
[http://dx.doi.org/10.1109/FITME.2008.35]
[127]
W. Zhang, Q. Chen, J. Yan, S. Zhang, and J. Xu, "A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting", Energy, vol. 236, p. 121492, 2021.
[http://dx.doi.org/10.1016/j.energy.2021.121492]
[128]
W. Zhang, Y. He, and S. Yang, "Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation", Electr. Power Syst. Res., vol. 201, p. 107551, 2021.
[http://dx.doi.org/10.1016/j.epsr.2021.107551]
[129]
W. Zhang, A. Maleki, and M.A. Rosen, "A heuristic-based approach for optimizing a small independent solar and wind hybrid power scheme incorporating load forecasting", J. Clean. Prod., vol. 241, p. 117920, 2019.
[http://dx.doi.org/10.1016/j.jclepro.2019.117920]
[130]
Z. Zhang, and W.C. Hong, "Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads", Knowl. Base. Syst., vol. 228, p. 107297, 2021.
[http://dx.doi.org/10.1016/j.knosys.2021.107297]
[131]
X. Zhao, W. Gao, F. Qian, and J. Ge, "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system", Energy, vol. 229, p. 120538, 2021.
[http://dx.doi.org/10.1016/j.energy.2021.120538]
[132]
X. Zheng, "Electric load forecasting using Bayesian Least Squares Support Vector Machine", 2010 Sixth International Conference on Natural Computation, Aug 10-12, 2010, Yantai, China, pp. 880-883, 2010.
[http://dx.doi.org/10.1109/ICNC.2010.5583914]
[133]
K. Zor, O. Timur, and A. Teke, "A state-of-the-art review of artificial intelligence techniques for short-term electric load forecasting", 2017 6th International Youth Conference on Energy (IYCE), , pp. 1-7, 2017.
[http://dx.doi.org/10.1109/IYCE.2017.8003734]
[134]
G.F. Fan, L.L. Peng, X. Zhao, and W.C. Hong, "Applications of hybrid EMD with PSO and GA for an SVR-based load forecasting model", Energies, vol. 10, no. 11, p. 1713, 2017.
[http://dx.doi.org/10.3390/en10111713]
[135]
S. Li, L. Goel, and P. Wang, "An ensemble approach for short-term load forecasting by extreme learning machine", Appl. Energy, vol. 170, pp. 22-29, 2016.
[http://dx.doi.org/10.1016/j.apenergy.2016.02.114]
[136]
T. Fang, and R. Lahdelma, "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system", Appl. Energy, vol. 179, pp. 544-552, 2016.
[http://dx.doi.org/10.1016/j.apenergy.2016.06.133]
[137]
M. Elsisi, M.Q. Tran, K. Mahmoud, D.E.A. Mansour, M. Lehtonen, and M.F. Darwish, "Effective IoT-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties", Measurement, vol. 190, p. 110686, 2022.
[http://dx.doi.org/10.1016/j.measurement.2021.110686]
[138]
M. Elsisi, and M.Q. Tran, "Development of an IoT architecture based on a deep neural network against cyber attacks for automated guided vehicles", Sensors , vol. 21, no. 24, p. 8467, 2021.
[http://dx.doi.org/10.3390/s21248467] [PMID: 34960561]
[139]
M. Elsisi, M.Q. Tran, H.M. Hasanien, R.A. Turky, F. Albalawi, and S.S.M. Ghoneim, "Robust model predictive control paradigm for automatic voltage regulators against uncertainty based on optimization algorithms", Mathematics, vol. 9, no. 22, p. 2885, 2021.
[http://dx.doi.org/10.3390/math9222885]
[140]
M.Q. Tran, M. Elsisi, M-K. Liu, V.Q. Vu, K. Mahmoud, M.M.F. Darwish, A.Y. Abdelaziz, and M. Lehtonen, "Reliable deep learning and IoT-based monitoring system for secure computer numerical control machines against cyber-attacks with experimental verification", IEEE Access, vol. 10, pp. 23186-23197, 2022.
[http://dx.doi.org/10.1109/ACCESS.2022.3153471]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy