[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 LSTMTransformer 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 timedomain 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 highproportion 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.