ALM: Lightweight Adaptation of Foundation Models for Joint Weather-Power Forecasting

Weather information is crucial for forecasting power variables such as wind power, photovoltaic power, and load. Existing power forecasting methods typically treat weather variables as exogenous inputs and generate site-level forecasts. However, such localized modeling makes it difficult to capture how continuous weather processes propagate across space, evolve over time, and persistently affect power dynamics. Weather foundation models are well suited to representing these continuous atmospheric processes, yet how to extend their forecasting capability to joint weather-power prediction remains insufficiently explored. To address these limitations, we propose $\text{ALM}$, a lightweight adaptation approach that supports weather-power joint forecasting and bidirectional information exchange between weather and power backbones. ALM consists of three steps: prefusion alignment, latent memory, and postfusion modulation, which support joint improvements in weather and power forecasting while keeping most backbone parameters frozen. We evaluate the method on a gridded weather-power benchmark for the contiguous United States (CONUS) and further validate its forecasts in downstream power applications. Compared with full fine-tuning, ALM reduces mean 72-hour RMSE by 19.9% across 12 reported variables while using only 9.7% trainable parameters. ALM also reaches the shortest per-epoch training time among all compared methods. Our code is available in the Supplementary Material.