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System Development & Application
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2767-2772

Load forecasting based on optimal feature combination improved XGBoost

Gu Yundong1,2
Liu Hao1
1. Xinjiang University of Finance & Economics, Urumqi 830012, China
2. School of Mathematics, North China Electric Power University, Beijing 102206, China

Abstract

To solve the limitation that nonlinear-correlation can't be effectively measured by conventional feature-selection method, this paper proposed an optimal feature combination improved XGBoost model. Firstly, it calculated the value of mutual information between the historical load and the load to be predicted, and formed the MI filter set by top K historical load with the largest MI value. Secondly, it formed SHAP filter set by top L features with calculated SHAP value from MI filter set. Finally, it improved the XGBoost by optimized K and L with PSO algorithm, and built the OFCI-XGBoost model successfully. The result shows that the error of proposed method is 1.11%, lower than the improved SVM, decision tree, ridge with the same strategy as proposed, which shows the method is effective to do load forecasting.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.12.0552
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 9
Section: System Development & Application
Pages: 2767-2772
Serial Number: 1001-3695(2021)09-038-2767-06

Publish History

[2021-09-05] Printed Article

Cite This Article

谷云东, 刘浩. 基于最优特征组合改进极限梯度提升的负荷预测 [J]. 计算机应用研究, 2021, 38 (9): 2767-2772. (Gu Yundong, Liu Hao. Load forecasting based on optimal feature combination improved XGBoost [J]. Application Research of Computers, 2021, 38 (9): 2767-2772. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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