Journal of Applied Science and Engineering

Published by Tamkang University Press


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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.

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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


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