An electricity forecasting method based on multi-type electricity decomposition prediction

Jun Liu; Wenbin Cai; Wei Bai; Xianglong Liu; Junjie Chen; Yuchen Zhang

doi:10.1117/12.3050885

12 December 2024 An electricity forecasting method based on multi-type electricity decomposition prediction

Jun Liu, Wenbin Cai, Wei Bai, Xianglong Liu, Junjie Chen, Yuchen Zhang

Author Affiliations +

Proceedings Volume 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024); 1341927 (2024) https://doi.org/10.1117/12.3050885
Event: Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 2024, Lhasa, China

Abstract

With the continuous development of the new power system, accurately predicting the total electricity consumption has become increasingly important. This paper proposes a daily electricity consumption forecasting method based on multi-type electricity consumption decomposition. First, the total electricity consumption is divided into base electricity consumption and temperature-controlled electricity consumption. The base electricity consumption is predicted by fitting it with economic indicators that show high Pearson correlation coefficients. Then, a genetic algorithm is used to optimize eXtreme Gradient Boosting parameters for accurately forecasting the rapidly changing temperature-controlled electricity consumption. Finally, the results of both predictions are linearly combined to obtain the total forecasted electricity consumption. Simulation results shows that the proposed method effectively predicts the daily electricity consumption for a provincial power grid in China, validating its effectiveness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Liu, Wenbin Cai, Wei Bai, Xianglong Liu, Junjie Chen, and Yuchen Zhang "An electricity forecasting method based on multi-type electricity decomposition prediction", Proc. SPIE 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 1341927 (12 December 2024); https://doi.org/10.1117/12.3050885

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