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A 24-Step Short-Term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions

Author

Listed:
  • Mingshen Xu

    (Department of Mathematics and Physics, North China Electric Power University, Baoding 071003, China)

  • Wanli Liu

    (Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China)

  • Shijie Wang

    (Department of Economic Management, North China Electric Power University, Baoding 071003, China)

  • Jingjia Tian

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Peng Wu

    (Engineering Training and Innovation and Entrepreneurship Education Center, North China Electric Power University, Baoding 071003, China)

  • Congjiu Xie

    (Pioneer Navigation Control Technology Co., Ltd., Hefei 230061, China)

Abstract

With the global objectives of achieving a “carbon peak” and “carbon neutrality” along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from 1 January to 30 December in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications.

Suggested Citation

  • Mingshen Xu & Wanli Liu & Shijie Wang & Jingjia Tian & Peng Wu & Congjiu Xie, 2024. "A 24-Step Short-Term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions," Energies, MDPI, vol. 17(18), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4742-:d:1483625
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    References listed on IDEAS

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