An Adaptive Transformer Model for Long‐Term Time‐Series Forecasting of Temperature and Radiation in Photovoltaic Energy Generation

Forecasting weather parameters, particularly temperature and solar radiation, plays a vital role in enhancing the efficiency of photovoltaic (PV) systems. This study introduces a cutting‐edge transformer‐based model specifically tailored for long‐term time‐series forecasting, aimed at improving the performance of PV generation systems. Leveraging the robust attention mechanisms and parallel processing capabilities inherent in encoder–decoder transformer architecture, the model effectively captures intricate relationships within weather data. A unique positional encoding layer is incorporated to bolster the model’s comprehension of the chronological sequence of data points. Furthermore, the multihead attention mechanism adeptly identifies interactions between key meteorological factors, especially temperature and radiation, which are crucial for precise PV generation predictions. Evaluation using real‐world weather datasets reveals that the proposed model significantly surpasses conventional forecasting methods in mean squared error and mean absolute error metrics. This work underscores the applicability of transformer models in predicting temperature and radiation for PV generation, offering a scalable and efficient forecasting solution vital for sustainable energy management. The model is suitable for both large‐scale solar installations and smaller setups, enhancing operational strategies and energy capture. Its improved accuracy in forecasting global horizontal radiation and temperature contributes to better planning and more effective energy utilization.