Short-Term Load Forecasting of Power System Based on VMD-CNN-BIGRU

doi:10.11930/j.issn.1004-9649.202206097

Abstract

Abstract: In order to improve the accuracy of load prediction, taking into account the internal laws and external influencing factors of historical load itself, a kind of variational modal decomposition (VMD) -convolutional neural networks (CNN) -bi-directional gated recurrent units are proposed. BIGRU) short-term load prediction method for hybrid networks, improving training duration and prediction results. The effectiveness of the proposed method is verified by simulation analysis, and the method has higher load prediction accuracy and stronger robustness than other models, which can improve the accuracy of short-term load prediction of power system.

Key words: short load forecasting, variational modal decomposition, convolutional neural network, bi-directional gated recurrent unit

YANG Huping, YU Yang, WANG Chao, LI Xiangjun, HU Yitao, RAO Chuchu. Short-Term Load Forecasting of Power System Based on VMD-CNN-BIGRU[J]. Electric Power, 2022, 55(10): 71-76.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202206097

https://www.electricpower.com.cn/EN/Y2022/V55/I10/71

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