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A Medium- and Long-Term Residential Load Forecasting Method Based on Discrete Cosine Transform-FEDformer

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  • Dengao Li

    (Shanxi Energy Internet Research Institute, Taiyuan 030000, China
    School of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China)

  • Qi Liu

    (Shanxi Energy Internet Research Institute, Taiyuan 030000, China
    School of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China)

  • Ding Feng

    (Shanxi Energy Internet Research Institute, Taiyuan 030000, China
    School of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
    School of Computer Science and Technology, Taiyuan Normal University, Taiyuan 030619, China)

  • Zhichao Chen

    (Shanxi Energy Internet Research Institute, Taiyuan 030000, China
    School of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model’s capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE.

Suggested Citation

  • Dengao Li & Qi Liu & Ding Feng & Zhichao Chen, 2024. "A Medium- and Long-Term Residential Load Forecasting Method Based on Discrete Cosine Transform-FEDformer," Energies, MDPI, vol. 17(15), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3676-:d:1442940
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    References listed on IDEAS

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    1. Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).
    2. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
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