Fan Wang This email address is being protected from spambots. You need JavaScript enabled to view it.1, Chen Chen1, Haitao Zhang1, and Youhua Ma2

1State Grid Xiongan New Area Electric Power Supply Company, Xiong’an New Area 071600, China
2Shanghai Electric Power Design Institute Co., Ltd, Shanghai 200025, China


Received: August 21, 2021
Accepted: February 14, 2022
Publication Date: April 5, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||  


To enrich short-term load forecasting methods and improve forecasting accuracy, a short-term load forecasting method based on variational mode decomposition and chaotic grey wolf optimization (CGWO) improved random forest (RF) is proposed. Firstly, the traditional GWO is improved by using the improved Logistic chaotic sequence and cooperative attack strategy, and then the CGWO is obtained. Secondly, the CGWO is used to optimize the decision trees and split features in the RF regression model to obtain an improved RF forecasting model. Thirdly, the short-term power load component is obtained by variational mode decomposition (VMD). Finally, the improved RF forecasting model is used for the prediction of short-term power load components, and the prediction results are reconstructed to obtain the final prediction results. The results show that the VMD-CGWO-RF method can effectively predict the short-term power load, the average absolute error is 48.76 megawatt(MW), the root mean square error is 59.53MW, the mean absolute percentage error is 0.66%, while the three indexes of the traditional RF method and CGWO-RF method are larger than VMD-CGWO-RF method, so the proposed method has higher forecasting accuracy.

Keywords: wolf swarm cooperative attack strategy; grey wolf optimization; random forest; variational mode decomposition; short-term load forecasting


  1. [1] A. D. Papalexopoulos, (1990) “A regression-based approach to short-term system load forecasting" IEEE Transactions on Power Systems 5(4): 1535–1547. DOI: 10.1109/59.99410.
  2. [2] K.-M. Im and J.-H. Lim. “A Design of Short-Term Load Forecasting Structure Based on ARIMA Using Load Pattern Classification”. In: Future Information Technology. Ed. by J. J. Park, L. T. Yang, and C. Lee. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, 296–303. DOI: 10.1007/978-3-642-22309-9_36.
  3. [3] Pappas, S. S., Ekonomou, L., Karamousantas, C. D., Chatzarakis, E. G., Katsikas, and K. a. S., (2008) “Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models" ENERGY OXFORD: DOI: 10.1016/
  4. [4] H. Tao, G. Min, M. E. Baran, and H. L.Willis, (2010) “Modeling and forecasting hourly electric load by multiple linear regression with interactions" IEEE: DOI: 10.1016/
  5. [5] S. J. Huang and K. R. Shih, (2003) “Short-term load forecasting via ARMA model identification including non-Gaussian process considerations" IEEE Transactions on Power Systems 18(2): 673–679. DOI: 10.1109/TPWRS.2003.811010.
  6. [6] Y. Chen, P. Xu, Y. Chu, W. Li, Y. Wu, L. Ni, Y. Bao, and K. Wang, (2017) “Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings" Applied Energy 195: 659–670. DOI: 10.1016/j.apenergy.2017.03.034.
  7. [7] Wang, Yinhai, Haiyang, Tao, Zhimin, Yunpeng, and Xiaolei, (2015) “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data" Transportation research, Part C. Emerging technologies 54C(May): 187–197. DOI: 10.1016/j.trc.2015.03.014.
  8. [8] M. Akhoondzadeh, (2016) “Decision Tree, Bagging and Random Forest methods detect TEC seismo-ionospheric anomalies around the time of the Chile, (Mw = 8.8) earthquake of 27 February 2010" Advances in Space Research 57(12): 2464–2469. DOI: 10.1016/j.asr.2016.03.035.
  9. [9] R. Harb, X. Yan, E. Radwan, and X. Su, (2009) “Exploring precrash maneuvers using classification trees and random forests" Accident Analysis & Prevention 41(1): 98–107. DOI:
  10. [10] A. X.-Y. P. Lu W. Lin and Z. Yu-Rong., (2021) “Forecasting Tourist Arrivals via Random Forest and Long Short-term Memory" Cognitive Computation 13: 125–138. DOI: 10.1007/s12559-020-09747-z.
  11. [11] J. Nan, F. Fei, Z. Hua, Z. Xiuping, and Z. Qinghe., (2020) “A Municipal PM2.5 Forecasting Method Based on Random Forest and WRF Model" Engineering Letters 28: 312–321.
  12. [12] S. Shabeerkhan and A. Padma, (2019) “A novel GWO optimized pruning technique for inexact circuit design" Microprocessors and Microsystems 73: 102975. DOI: 10.1016/j.micpro.2019.102975.
  13. [13] L. Breiman, (2001) “Random forests" Mach. Learn 45: 5–32.
  14. [14] Z. Zheng, P. Lu, and D. Tolliver, (2016) “Accident prediction for highway-rail grade crossings using decision tree approach: An empirical analysis" Transp. Res. Rec.J. Transp. Res. Board 2545: 115–122.
  15. [15] C. R. Sekhar, Minal, and E. Madhu, (2016) “Mode Choice Analysis Using Random Forrest Decision Trees" Transportation Research Procedia: DOI: 10.1016/j.trpro.2016.11.119.
  16. [16] S. Daga, TorgynShaikhina, D. Lowe, D. Briggs, and N. Khovanova, (2002) “Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation" Biomedical Signal Processing & Control 37(10): 1025–1042. DOI: 10.1016/j.bspc.2017.01.012.
  17. [17] L. Breiman, J. Friedman, R. Olshen, and C. Stone, (2015) “Classification and Regression Trees. Belmont, CA: Wadsworth International Group." Encyclopedia of Ecology 57(1): 582–588.
  18. [18] X. Zhang, X. Wang, H. Chen, D. Wang, and Z. Fu, (2020) “Improved GWO for large-scale function optimization and MLP optimization in cancer identification" Neural Computing and Applications 32(5): 1305–1325. DOI: 10.1007/s00521-019-04483-4.
  19. [19] A. Saxena, R. Kumar, and S. Das, (2018) “β-Chaotic map enabled Grey Wolf Optimizer" Applied Soft Computing: DOI: 10.1016/j.asoc.2018.10.044.
  20. [20] X. Zhao, F. Yang, Y. Han, and Y. Cui, (2020) “An Opposition-Based Chaotic Salp Swarm Algorithm for Global Optimization" IEEE Access 8: 36485–36501. DOI: 10.1109/ACCESS.2020.2976101.


42nd percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.