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

Guoyong Zhang, Haochuan Li (), Lingli Wang, Weiying Wang, Jun Guo, Hui Qin and Xiu Ni
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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

Energies, 2024, vol. 17, issue 22, 1-22

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.

Keywords: hydropower generation; transformer; SE-Attention; LSTM; medium- and long-term forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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