PVFM: A Solar-Aware, Weather-Conditioned Foundation Model for Zero-Shot Photovoltaic Forecasting

Rapid PV deployment across regions and installation types demands accurate and scalable power forecasting, particularly for newly commissioned plants. Existing forecasters are often site- and task-specific and transfer poorly to unseen sites, while general-purpose TSFMs show limited advantages in short-horizon and high-resolution PV forecasting, leaving unified forecasting across diverse PV scenarios an open challenge. We introduce PVFM, a 3.88M-parameter weather-conditioned foundation model for zero-shot PV power forecasting. The model separately models historical observations and future weather covariates through dedicated information streams, augmented with solar-aware embeddings that encode solar geometry and static identity information. It adopts a general-to-specific strategy, first pretraining on 59.2 million PV observations across global sites with weather covariates from multiple sources. The pretrained model is further evaluated under ramp events and extended through multimodal fine-tuning with satellite imagery for short-horizon forecasting. Across five zero-shot tasks spanning short- to long-term horizons at 1-hour and 15-minute resolutions, PVFM consistently outperforms both full-shot models and existing TSFMs, reducing MAE by 10.2-24.3% and CRPS by 10.0-29.5%. Satellite-image fine-tuning further reduces RMSE by 9.1% in ultra-short-term forecasting.