SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting
This study addresses the challenge of insufficient accuracy in short-term solar power forecasting by introducing, for the first time, the Transformer architecture to this task. Leveraging its self-attention mechanism, the proposed model effectively captures both temporal dependencies and spatial variations in solar irradiance, while incorporating power plant metadata to enhance generalization. Within a unified framework, the method achieves high-precision predictions across diverse sites and seasons, demonstrating consistently superior robustness and generalization performance compared to existing models under varied weather conditions—including clear skies and overcast days.
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