PhysTrans: A Physics-Aware Transferable Framework for Global Cold-Start Photovoltaic Forecasting

Abstract

With the rapid expansion of photovoltaic (PV) power generation worldwide, PV systems have become key to global energy construction. Accurate PV forecasting is essential for safe grid operation and renewable energy integration. However, most existing models rely heavily on site-specific historical data and perform poorly when deployed in cold-start scenarios of newly built power plants. We propose PhysTrans, a physics-aware transferable framework for cold-start PV forecasting. Firstly, we design a physics-constrained residual network that utilizes a clear-sky module for better physical consistency. Furthermore, we propose a dynamic cloud cropping method to obtain the cloud information of shaded PV stations by fitting the angle of the sun offsets. To fuse the asymmetric data, a query-based asymmetric fusion mechanism is introduced to achieve high-precision alignment of multi-modal data. We conduct experiments on global datasets, and the results show that the PhysTrans outperforms state-of-the-art models with a 13.2% decrease in MAE in the single-site task, and also outperforms existing migration models with an average decrease in MAE of 12.7% in the cross-sites task. Our work advances reliable and transferable PV forecasting for early-stage grid integration and contributes to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), in line with the Leave No One Behind principle.