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A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism

Author

Listed:
  • Qingbo Hua

    (Qingdao Elink Information Technology Co., Ltd., Qingdao 266033, China)

  • Zengliang Fan

    (Qingdao Elink Information Technology Co., Ltd., Qingdao 266033, China)

  • Wei Mu

    (Qingdao Elink Information Technology Co., Ltd., Qingdao 266033, China)

  • Jiqiang Cui

    (School of Automation, Qingdao University, Qingdao 266100, China)

  • Rongxin Xing

    (School of Automation, Qingdao University, Qingdao 266100, China)

  • Huabo Liu

    (School of Automation, Qingdao University, Qingdao 266100, China)

  • Junwei Gao

    (School of Automation, Qingdao University, Qingdao 266100, China)

Abstract

This paper proposes a short-term electric load forecasting method combining convolutional neural networks and gated recurrent units with an attention mechanism. By integrating CNNs and GRUs, the method can fully leverage the strengths of CNNs in feature extraction and the advantages of GRUs in sequence modeling, enabling the model to comprehend signal data more comprehensively and effectively extract features from sequential data. The introduction of the attention mechanism allows the traditional model to dynamically focus on important parts of the input data while ignoring the unimportant parts. This capability enables the model to utilize input information more efficiently, thereby enhancing model performance. This paper applies the proposed model to a dataset comprising regional electric load and meteorological data for experimentation. The results show that compared with other common models, the proposed model effectively reduces the mean absolute error and root mean square error (121.51 and 263.43, respectively) and accurately predicts the short-term change in regional power load.

Suggested Citation

  • Qingbo Hua & Zengliang Fan & Wei Mu & Jiqiang Cui & Rongxin Xing & Huabo Liu & Junwei Gao, 2024. "A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism," Energies, MDPI, vol. 18(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:18:y:2024:i:1:p:106-:d:1556996
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    References listed on IDEAS

    as
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    4. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
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