Research on New Energy Power Generation Forecasting Method Based on Bi-LSTM and Transformer

Hao He, Wei He, Jun Guo (), Kang Wu, Weizhe Zhao and Zijing Wan
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Hao He: Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China
Wei He: Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China
Jun Guo: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Kang Wu: Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China
Weizhe Zhao: Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China
Zijing Wan: Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China

Energies, 2025, vol. 18, issue 19, 1-25

Abstract: With the increasing penetration of wind and photovoltaic (PV) power in modern power systems, accurate power forecasting has become crucial for ensuring grid stability and optimizing dispatch strategies. This study focuses on multiple wind farms and PV plants, where three deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and a hybrid Transformer–BiLSTM model—are constructed and systematically compared to enhance forecasting accuracy and dynamic responsiveness. First, the predictive performance of each model across different power stations is analyzed. The results reveal that the LSTM model suffers from systematic bias and lag effects in extreme value ranges, while Bi-LSTM demonstrates advantages in mitigating time-lag issues and improving dynamic fitting, achieving on average a 24% improvement in accuracy for wind farms and a 20% improvement for PV plants compared with LSTM. Moreover, the Transformer–BiLSTM model significantly strengthens the ability to capture complex temporal dependencies and extreme power fluctuations. Experimental results indicate that the Transformer–BiLSTM consistently delivers higher forecasting accuracy and stability across all test sites, effectively reducing extreme-value errors and prediction delays. Compared with Bi-LSTM, its average accuracy improves by 19% in wind farms and 35% in PV plants. Finally, this paper discusses the limitations of the current models in terms of multi-source data fusion, outlier handling, and computational efficiency, and outlines directions for future research. The findings provide strong technical support for renewable energy power forecasting, thereby facilitating efficient scheduling and risk management in smart grids.

Keywords: renewable energy generation; deep learning; LSTM; Bi-LSTM; transformer; power forecasting model (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: 2025
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