Short-term power load forecasting method based on VMD-GRU-EC
LI Feihong, XIAO Yingqun
1. School of Electrical Engineering, Guizhou University, Guiyang 550025, China;
2. School of Big Data, Guizhou Institute of Technology, Guiyang 550003, China
Abstract: To improve the accuracy of load forecasting, this paper proposes a short-term power load forecasting method based on VMD-GRU-EC. In view of the nonlinear and non-stationary characteristics of the original load sequence, the VDM decomposition method is used to decompose the original load sequence to obtain several sub-sequences, and the GRU model distribution is used to establish a prediction model for each sub-sequence, and finally the predicted values of each sub-sequence are added to get The initial predicted value of the load series. After the initial prediction of the load sequence is obtained, the error sequence can be obtained, and the VMD-GRU model is also used to predict the error sequence. After using the VMD-GRU model to obtain the initial predicted load and error sequence in turn, the final predicted load is obtained through error correction (EC). The experimental study shows that among all the prediction models, the prediction method proposed in this paper has the highest prediction accuracy and the strongest stability. The effectiveness and superiority of the proposed model are verified.
Keywords: load forecasting;variational modal decomposition;gated recurrent unit;error correction
2023, 49(9):120-127  收稿日期: 2022-5-20;收到修改稿日期: 2022-6-29
基金项目:
作者简介: 李飞宏（1993-），男，四川自贡市人，硕士研究生，专业方向为深度学习、电力系统负荷预测。
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