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Two-Stage Short-Term Power Load Forecasting Based on RFECV Feature Selection Algorithm and a TCN–ECA–LSTM Neural Network

Author

Listed:
  • Hui Liang

    (School of Electrical Engineering, Xinjiang University, Urumqi 830017, China)

  • Jiahui Wu

    (School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
    Engineering Research Center of Renewable Energy Power Generation and Grid Connection Control, Ministry of Education, Urumqi 830017, China)

  • Hua Zhang

    (CGN New Energy Investment (Shenzhen) Co., Ltd., Xinjiang Branch, Urumqi 830011, China)

  • Jian Yang

    (CGN New Energy Investment (Shenzhen) Co., Ltd., Xinjiang Branch, Urumqi 830011, China)

Abstract

To solve the problem of feature selection and error correction after mode decomposition and improve the ability of power load forecasting models to capture complex time series information, a two-stage short-term power load forecasting method based on recursive feature elimination with a cross validation (RFECV) algorithm and time convolution network–efficient channel attention mechanism–long short-term memory network (TCN–ECA–LSTM) is presented. First, the load sequence is decomposed into a relatively stable set of modal components using variational mode decomposition. Then, the RFECV-based method filters the feature set of each modal component to construct the best feature set. Finally, a two-stage prediction model based on TCN–ECA–LSTM is established. The first stage predicts each modal component and the second stage reconstructs the load forecast based on the predicted value of the previous stage. This paper takes actual data from New South Wales, Australia, as an example, and the results show that the method proposed in this paper can build the feature set reliably and efficiently and has a higher accuracy than the conventional prediction model.

Suggested Citation

  • Hui Liang & Jiahui Wu & Hua Zhang & Jian Yang, 2023. "Two-Stage Short-Term Power Load Forecasting Based on RFECV Feature Selection Algorithm and a TCN–ECA–LSTM Neural Network," Energies, MDPI, vol. 16(4), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1925-:d:1069142
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    References listed on IDEAS

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    1. He, Feifei & Zhou, Jianzhong & Feng, Zhong-kai & Liu, Guangbiao & Yang, Yuqi, 2019. "A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm," Applied Energy, Elsevier, vol. 237(C), pages 103-116.
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