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Short-term power load forecasting in China: A Bi-SATCN neural network model based on VMD-SE

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  • Yuan Huang
  • Qimeng Feng
  • Feilong Han

Abstract

This study focuses on improving short-term power load forecasting, a critical aspect of power system planning, control, and operation, especially within the context of China’s "dual-carbon" policy. The integration of renewable energy under this policy has introduced complexities such as nonlinearity and instability. To enhance forecasting accuracy, the VMD-SE-BiSATCN prediction model is proposed. This model improves computational efficiency and reduces prediction errors by analyzing and reconstructing sequence component complexity using sample entropy (SE) following variational mode decomposition (VMD). Additionally, a self-attention mechanism is integrated into the temporal convolutional network (TCN) to overcome the traditional TCN’s limitations in capturing long-term dependencies. The model was evaluated using data from the China Ninth Electrical Attribute Modeling Competition and validated with real-world data from a specific county in Shijiazhuang City, Hebei Province, China. Results indicate that the VMD-SE-BiSATCN model outperforms other models, achieving a mean absolute error (MAE) of 92.87, a root mean square error (RMSE) of 126.906, and a mean absolute percentage error (MAPE) of 0.81%.

Suggested Citation

  • Yuan Huang & Qimeng Feng & Feilong Han, 2024. "Short-term power load forecasting in China: A Bi-SATCN neural network model based on VMD-SE," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-24, September.
    • Handle: RePEc:plo:pone00:0311194
    DOI: 10.1371/journal.pone.0311194
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    References listed on IDEAS

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    1. Shizhen Bai & Hao He & Dan Luo & Mengke Ge & Ruobing Yang & Xinrui Bi & Zeljko Stevic, 2022. "A Large-Scale Group Decision-Making Consensus Model considering the Experts’ Adjustment Willingness Based on the Interactive Weights’ Determination," Complexity, Hindawi, vol. 2022, pages 1-26, November.
    2. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network," Energy, Elsevier, vol. 233(C).
    3. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
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