Grey correlation-oriented Random Forest and Particle Swarm Optimization Algorithm for Power Load Forecasting

Xiao Xiong; Zengyong Xu; Yuxia Yuan

doi:10.6180/jase.202202_25(1).0003

Grey correlation-oriented Random Forest and Particle Swarm Optimization Algorithm for Power Load Forecasting

Xiao Xiong¹, Zengyong Xu², and Yuxia Yuan This email address is being protected from spambots. You need JavaScript enabled to view it.³

¹Zhengzhou Electric Power College, Zhengzhou 450000,China²Henan College of Transportation, Zhengzhou 450000,China³School of Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou 450000,China

Received: November 9, 2020
Accepted: May 3, 2021
Publication Date: July 5, 2021

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: ||https://doi.org/10.6180/jase.202202_25(1).0003

ABSTRACT

Accurate power load prediction plays an important role in the design of power distribution equipment and distribution network. The traditional forecasting methods have the problems with low accuracy of power load forecasting and slow model training. In order to improve the accuracy of power load fore-casting, this paper proposes a new method combining Grey correlation-oriented random forest with par-ticle swarm optimization algorithm for power load prediction. The method first uses Grey correlation projection to measure the similarity between the attributes of historical samples and the attributes of predicted samples, and it constructs the similar historical sample data set. Then the decision tree of ran-dom forest is optimized based on particle swarm optimization to improve the prediction accuracy. Fi-nally, Hadoop distributed cluster is used to realize the parallelization of power load prediction and im-prove the prediction efficiency. The experimental results show that the proposed model in this paper has better prediction performance than the traditional power load forecasting methods.

Keywords: Power load prediction, Grey correlation, Random forest, Particle swarm optimization

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