Research on power purchase forecasting technology based on EEMD-GRU-RF

Xiaomin Li; Ming Sun; Yu Li; Yuanyuan Wang

doi:10.1117/12.3015376

6 February 2024 Research on power purchase forecasting technology based on EEMD-GRU-RF

Xiaomin Li, Ming Sun, Yu Li, Yuanyuan Wang

Proceedings Volume 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023); 129794T (2024) https://doi.org/10.1117/12.3015376
Event: 9th International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 2023, Guilin, China

Abstract

The power purchase forecast can help the power system make reasonable power pre-purchase plans and achieve scientific coordination of grid planning and construction. This paper proposes a forecast model of power purchase based on EEMD-GRU-RF. The model first decomposes the original power purchase series into high and low frequency components using EEMD, and then selects the random forest algorithm to predict the low frequency components of power purchase using meteorological, fault work order, historical power consumption series and holiday information as features, and uses GRU network to achieve the prediction of high frequency eigenmodes, and finally reconstructs the superposition of both to achieve power purchase prediction. The experimental results show that the evaluation indexes RMSE, MAE and R2 of the proposed algorithm are 0.019, 0.011 and 0.976, respectively, which are better than other mainstream power purchase prediction algorithms and can achieve higher accuracy of power purchase prediction.

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

Citation Download Citation

Xiaomin Li, Ming Sun, Yu Li, and Yuanyuan Wang "Research on power purchase forecasting technology based on EEMD-GRU-RF", Proc. SPIE 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 129794T (6 February 2024); https://doi.org/10.1117/12.3015376

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