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A VMD and LSTM based hybrid model of load forecasting for power grid security

Lv, Lingling; Wu, Zongyu; Zhang, Jinhua; Tan, Zhiyuan; Zhang, Lei; Tian, Zhihong


Lingling Lv

Zongyu Wu

Jinhua Zhang

Lei Zhang

Zhihong Tian


As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply-demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on Vari-ational mode decomposition (VMD) and Long Short-Term Memory (LSTM) with seasonal factors elimination and error correction is proposed in this paper. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models. Index Terms-Power grid security, short-term load forecasting , seasonal factors elimination, error correction.

Journal Article Type Article
Acceptance Date Nov 20, 2021
Online Publication Date Nov 24, 2021
Publication Date 2022-09
Deposit Date Nov 26, 2021
Publicly Available Date Nov 26, 2021
Journal IEEE Transactions on Industrial Informatics
Print ISSN 1551-3203
Electronic ISSN 1941-0050
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 18
Issue 9
Pages 6474-6482
Keywords Power grid security, short-term load forecasting, seasonal factors elimination, error correction
Public URL


A VMD And LSTM Based Hybrid Model Of Load Forecasting For Power Grid Security (accepted version) (429 Kb)

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