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Research on Medium- and Long-Term Hydropower Generation Forecasting Method Based on LSTM and Transformer

Author

Listed:
  • Guoyong Zhang

    (China Renewable Energy Engineering Institute, Beijing 100011, China)

  • Haochuan Li

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Lingli Wang

    (China Renewable Energy Engineering Institute, Beijing 100011, China)

  • Weiying Wang

    (China Renewable Energy Engineering Institute, Beijing 100011, China)

  • Jun Guo

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Hui Qin

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Xiu Ni

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Hydropower generation is influenced by various factors such as precipitation, temperature, and installed capacity, with hydrometeorological factors exhibiting significant temporal variability. This study proposes a hydropower generation forecasting method based on Transformer and SE-Attention for different provinces. In the model, the outputs of the Transformer and SE-Attention modules are fed into an LSTM layer to capture long-term data dependencies. The SE-Attention module is reintroduced to enhance the model’s focus on important temporal features, and a linear layer maps the hidden state of the last time step to the final output. The proposed Transformer-LSTM-SE model was tested using provincial hydropower generation data from Yunnan, Sichuan, and Chongqing. The experimental results demonstrate that this model achieves high accuracy and stability in medium- and long-term hydropower forecasting at the provincial level, with an average accuracy improvement of 33.79% over the LSTM model and 24.30% over the Transformer-LSTM model.

Suggested Citation

  • Guoyong Zhang & Haochuan Li & Lingli Wang & Weiying Wang & Jun Guo & Hui Qin & Xiu Ni, 2024. "Research on Medium- and Long-Term Hydropower Generation Forecasting Method Based on LSTM and Transformer," Energies, MDPI, vol. 17(22), pages 1-22, November.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5707-:d:1521261
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    References listed on IDEAS

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    2. John W. Galbraith & Victoria Zinde-Walsh, 2001. "Autoregression-Based Estimators for ARFIMA Models," CIRANO Working Papers 2001s-11, CIRANO.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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