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A hybrid power load forecasting model using BiStacking and TCN-GRU

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  • Jun Ma
  • Jishen Peng
  • Haotong Han
  • Liye Song
  • Hao Liu

Abstract

Accurate power load forecasting helps reduce energy waste and improve grid stability. This paper proposes a hybrid forecasting model, BiStacking+TCN-GRU, which leverages both ensemble learning and deep learning techniques. The model first applies the Pearson correlation coefficient (PCC) to select features highly correlated with the power load. Then, BiStacking is used for preliminary predictions, followed by a temporal convolutional network (TCN) enhanced by a gated recurrent unit (GRU) to produce the final predictions. The experimental validation based on Panama’s 2020 electricity load data demonstrated the effectiveness of the model, with the model achieving an RMSE of 29.1213 and an MAE of 22.5206, respectively, with an R² of 0.9719. These results highlight the model’s superior performance in short-term load forecasting, demonstrating its strong practical applicability and theoretical contributions.

Suggested Citation

  • Jun Ma & Jishen Peng & Haotong Han & Liye Song & Hao Liu, 2025. "A hybrid power load forecasting model using BiStacking and TCN-GRU," PLOS ONE, Public Library of Science, vol. 20(4), pages 1-27, April.
    • Handle: RePEc:plo:pone00:0321529
    DOI: 10.1371/journal.pone.0321529
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    References listed on IDEAS

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    1. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
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