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Short-Term Power Load Forecasting Using a VMD-Crossformer Model

Author

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  • Siting Li

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

  • Huafeng Cai

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

Abstract

There are several complex and unpredictable aspects that affect the power grid. To make short-term power load forecasting more accurate, a short-term power load forecasting model that utilizes the VMD-Crossformer is suggested in this paper. First, the ideal number of decomposition layers was ascertained using a variational mode decomposition (VMD) parameter optimum approach based on the Pearson correlation coefficient (PCC). Second, the original data was decomposed into multiple modal components using VMD, and then the original data were reconstructed with the modal components. Finally, the reconstructed data were input into the Crossformer network, which utilizes the cross-dimensional dependence of multivariate time series (MTS) prediction; that is, the dimension-segment-wise (DSW) embedding and the two-stage attention (TSA) layer were designed to establish a hierarchical encoder–decoder (HED), and the final prediction was performed using information from different scales. The experimental results show that the method could accurately predict the electricity load with high accuracy and reliability. The MAE, MAPE, and RMSE were 61.532 MW, 1.841%, and 84.486 MW, respectively, for dataset I. The MAE, MAPE, and RMSE were 68.906 MW, 0.847%, and 89.209 MW, respectively, for dataset II. Compared with other models, the model in this paper predicted better.

Suggested Citation

  • Siting Li & Huafeng Cai, 2024. "Short-Term Power Load Forecasting Using a VMD-Crossformer Model," Energies, MDPI, vol. 17(11), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2773-:d:1409391
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    References listed on IDEAS

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    1. Guijuan Wang & Xinheng Wang & Zuoxun Wang & Chunrui Ma & Zengxu Song, 2021. "A VMD–CISSA–LSSVM Based Electricity Load Forecasting Model," Mathematics, MDPI, vol. 10(1), pages 1-28, December.
    2. Dai, Yeming & Zhao, Pei, 2020. "A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization," Applied Energy, Elsevier, vol. 279(C).
    3. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
    4. Fachrizal Aksan & Vishnu Suresh & Przemysław Janik & Tomasz Sikorski, 2023. "Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models," Energies, MDPI, vol. 16(14), pages 1-24, July.
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