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Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm

Author

Listed:
  • Xinjian Xiang

    (School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Tianshun Yuan

    (School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Guangke Cao

    (Key Laboratory of Intelligent Operation and Maintenance Robot of Zhejiang Province, Hangzhou Shenhao Technology, Hangzhou 311121, China)

  • Yongping Zheng

    (School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)

Abstract

In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA.

Suggested Citation

  • Xinjian Xiang & Tianshun Yuan & Guangke Cao & Yongping Zheng, 2024. "Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm," Energies, MDPI, vol. 17(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1815-:d:1373145
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    References listed on IDEAS

    as
    1. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
    2. Hongbin Xu & Qiang Peng & Yuhao Wang & Zengwen Zhan, 2023. "Power-Load Forecasting Model Based on Informer and Its Application," Energies, MDPI, vol. 16(7), pages 1-14, March.
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