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

Abstract

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.

Citation

Lv, L., Wu, Z., Zhang, J., Tan, Z., Zhang, L., & Tian, Z. (2022). A VMD and LSTM based hybrid model of load forecasting for power grid security. IEEE Transactions on Industrial Informatics, 18(9), 6474-6482. https://doi.org/10.1109/tii.2021.3130237

• 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
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 9
• Pages: 6474-6482
• DOI: https://doi.org/10.1109/tii.2021.3130237
• Keywords: Power grid security, short-term load forecasting, seasonal factors elimination, error correction
• Public URL: http://researchrepository.napier.ac.uk/Output/2824609