Physics-informed AI for next-generation PV forecasting and renewable energy integration

Short-term photovoltaic (PV) generation forecasting faces growing challenges that are highly relevant to the future of renewable energy integration. The increasing penetration of distributed small-scale PV systems adds heterogeneity and unpredictability to grid operations, straining traditional forecasting methods. Meanwhile, cutting-edge AI approaches demand significant computational resources, limiting real-time or edge deployment. Moreover, environmental variability – particularly dynamic cloud cover – further reduces forecasting accuracy, as current gradient-descent–based models struggle with non-linear, location-specific conditions. Finally, the growing complexity and opacity of AI models raises issues of interpretability, trust, and control. Addressing these challenges is scientifically critical for ensuring reliable, efficient, and transparent PV forecasting systems that can support stable renewable-powered grids.

 

This proposal put forward a set of targeted, well-defined strategies aimed at addressing the identified challenges by using a sensor network and physics-informed AI. The proposed solutions advance PV forecasting beyond purely data-driven AI by integrating physical knowledge and sensor-based observations. Physics-informed AI enhances interpretability, reduces non-linear complexity, and allows modular, generalizable models that align better with real-world system behavior. Meanwhile, sensor-network–based forecasting leverages distributed, real-time data to capture spatiotemporal variability and heterogeneity in PV generation, improving accuracy and resilience in decentralized grids. Together, these approaches represent a shift toward transparent, efficient, and physically grounded forecasting methods that are crucial for reliable renewable energy integration.