SPIRIT: Short-Term Prediction of Solar IRradIance for Transfer Learning Using Foundation Models

Aditya Mishra, T Ravindra, Srinivasan Iyengar, Shivkumar Kalyanaraman, Ponnurangam Kumaraguru

Keywords: Solar Irradiance Forecasting, Transfer Learning, Foundation Models, Zero-Shot Learning, Data-Efficient AI, Renewable Energy Integration, Climate Change Mitigation, Grid Management

Abstract: Traditional solar forecasting models are based on several years of site-specific historical irradiance data, often spanning five or more years, which are unavailable for newer photovoltaic farms. As renewable energy is highly intermittent, building accurate solar irradiance forecasting systems that are data-efficient is essential for efficient grid management and enabling the ongoing proliferation of solar energy, which is crucial to achieve the United Nations' net zero goals. In this work, we propose SPIRIT, a novel framework leveraging foundation models for solar irradiance forecasting, making it applicable to newer solar installations. Our approach outperforms state-of-the-art models in zero-shot transfer learning by upto 70\%, enabling effective performance at new locations without relying on any past data. Further improvements in performance are achieved through fine-tuning, as more location-specific data becomes available. These findings are supported by statistical significance, further validating our approach. By dramatically reducing the forecasting setup timeline, SPIRIT accelerates solar farm deployment in all potential global sites, most of which lack historical data, thereby democratizing access to clean energy and enabling participation in the renewable energy transition.

Submission Number: 23