热力发电2026，Vol.55Issue(8)：12-20,9.DOI:10.19666/j.rlfd.202603004
基于LSTM-Transformer混合模型的电力现货市场日前电价预测
Day-ahead electricity price forecasting for electricity spot market based on hybrid LSTM-Transformer model
摘要
Abstract
[Objective]The large-scale integration of renewable energy into the power grid has significantly exacerbated the risks of volume and price volatility in the electricity spot market,thereby creating an urgent need to improve electricity price forecasting capability.To address this challenge,this study constructs a hybrid LSTM-Transformer model based on actual operational data from Shandong Province,China.The primary aim is to achieve accurate day-ahead electricity price forecasting,thereby providing robust support for market operation optimization and risk management and control.[Methods]First,in the process of multi-dimensional feature fusion,the operational mechanisms of key influencing factors on electricity prices are systematically analyzed.The Pearson correlation coefficient is employed to quantify the linear correlation strength between these factors and electricity prices.This approach effectively resolves the problems inherent in traditional methods,such as unclear forecasting mechanisms and insufficient accuracy,which typically arise from relying on single features or empirical judgments.Second,the Transformer model is optimized by integrating LSTM,self-attention,and multi-layer attention mechanisms.This integration establishes a hierarchical framework that combines the strengths of sequential feature extraction and global dependency modeling.As a result,the model effectively enhances the ability to capture the complex long-and short-term dependencies embedded in electricity price sequences.Furthermore,a strategy of"deep mining of time-domain features coupled with frequency-domain loss constraints"is adopted.By fusing time-domain and frequency-domain losses,the model improves its fitting accuracy for both price fluctuations and numerical values.Meanwhile,a similar-day model is introduced to construct feature samples that closely reflect real-world conditions,which further enhances the forecasting accuracy.[Results]Through comprehensive case study analyses,the results demonstrate that the proposed model operates stably even under market conditions characterized by complex influencing factors and severe price fluctuations.The model significantly improves the forecasting accuracy of day-ahead electricity prices compared to conventional approaches.Quantitative evaluations show that the model achieves superior performance in tracking price trends and capturing sudden price spikes,which are common in high-renewable penetration scenarios.[Conclusion]The constructed LSTM-Transformer hybrid model,combined with multi-dimensional feature fusion and the time-frequency domain loss constraint strategy,can effectively address the challenges of volume and price fluctuations brought about by the high-proportion integration of renewable energy.It provides a high-precision and high-robustness solution for day-ahead electricity price forecasting in the electricity spot market,thereby contributing to more stable and efficient market operations.关键词
电价预测/长短时记忆神经网络/Transformer模型/多维影响因素Key words
electricity price forecasting/long short-term memory/Transformer model/multi-dimensional influencing factors引用本文复制引用
朱润泽,王德军,张宗兴,张佑..基于LSTM-Transformer混合模型的电力现货市场日前电价预测[J].热力发电,2026,55(8):12-20,9.基金项目
国家能源集团山东电力有限公司2024年科技项目(GJNYSD-2024-13) Science and Technology Innovation Project of National Energy Group Shandong Electric Power Co.,Ltd.in 2024(GJNYSD-2024-13) （GJNYSD-2024-13）
