W2P-FM: Learning Transferable Weather-to-Power Representations for Wind Power Forecasting Across Sites and Tasks

As wind power expands globally, accurate forecasting is critical for managing weather-driven generation variability and enabling its reliable integration into modern energy systems. Yet, continued wind-farm deployment and diverse forecasting demand across horizons and temporal granularities expose a fundamental limitation of conventional data-driven models, which are typically optimized on specific sites and tasks. Meeting these requirements calls for transferable modeling of weather-driven power dynamics while addressing three key challenges: aligning multi-source weather and power data, capturing cross-site heterogeneity, and supporting diverse forecasting tasks within a unified framework. To tackle these challenges, we propose W2P-FM, a foundation model for wind power forecasting pretrained on more than 20 million time points. W2P-FM constructs a uniform temporal-variable representation by using MeteoRouter to adaptively fuse numerical weather predictions and granularity-aware patching to align power and meteorological sequences. It further incorporates rich static site context and recent response observations to characterize site-specific patterns. Finally, weather-to-power representations are learned through intra-patch cross-variable interactions and local multi-scale inter-patch aggregation, with task-specific heads supporting different forecasting settings. Experiments on 33 real-world datasets comprising over 1,000 site-level wind power series across five continents show that W2P-FM consistently outperforms state-of-the-art forecasting models, reducing MSE by 12.3% in full-shot and 48.9% in zero-shot settings. Frozen-backbone adaptation reduces MSE by 17.2% in cross-granularity forecasting and CRPS by 15.8% in probabilistic forecasting relative to the strongest task-specific baselines.