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Advanced Short-Term Load Forecasting with XGBoost-RF Feature Selection and CNN-GRU.

Authors :
Cui, Jingping
Kuang, Wei
Geng, Kai
Bi, Aiying
Bi, Fengjiao
Zheng, Xiaogang
Lin, Chuan
Source :
Processes; Nov2024, Vol. 12 Issue 11, p2466, 19p
Publication Year :
2024

Abstract

Accurate and efficient short-term load forecasting (STLF) is essential for optimizing power system operations. This study proposes a novel hybrid forecasting model that integrates XGBoost-RF feature selection with a CNN-GRU neural network to enhance prediction performance while reducing model complexity. The XGBoost-RF approach is first applied to select the most predictive features from historical load data, weather conditions, and time-based variables. A convolutional neural network (CNN) is then employed to extract spatial features, while a gated recurrent unit (GRU) captures temporal dependencies for load forecasting. By leveraging a dual-channel structure that combines long- and short-term historical load trends, the proposed model significantly mitigates cumulative errors from recursive predictions. Experimental results demonstrate that the model achieves superior performance with an average root mean square error (RMSE) of 53.29 and mean absolute percentage error (MAPE) of 3.56% on the test set. Compared to traditional models, the prediction accuracy improves by 28.140% to 110.146%. Additionally, the model exhibits strong robustness across different climatic conditions. This research validates the efficacy of integrating XGBoost-RF feature selection with CNN-GRU for STLF, offering reliable decision support for power system management. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
22279717
Volume :
12
Issue :
11
Database :
Complementary Index
Journal :
Processes
Publication Type :
Academic Journal
Accession number :
181203732
Full Text :
https://doi.org/10.3390/pr12112466