Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters
This addresses the data scarcity problem for offshore wind farm operators and grid managers, enabling reliable forecasting without waiting for a full year of local measurements, though it appears to be an incremental improvement over existing transfer learning approaches.
The paper tackles the problem of accurate power forecasting for newly commissioned offshore wind farms that lack sufficient site-specific data by proposing a transfer learning framework that clusters meteorological features and uses an ensemble of expert models. The method achieves accurate cross-domain forecasting with under five months of site-specific data, with experiments showing a MAE of 3.52% across eight offshore wind farms.
Ambitious decarbonisation targets are catalysing growth in orders of new offshore wind farms. For these newly commissioned plants to run, accurate power forecasts are needed from the onset. These allow grid stability, good reserve management and efficient energy trading. Despite machine learning models having strong performances, they tend to require large volumes of site-specific data that new farms do not yet have. To overcome this data scarcity, we propose a novel transfer learning framework that clusters power output according to covariate meteorological features. Rather than training a single, general-purpose model, we thus forecast with an ensemble of expert models, each trained on a cluster. As these pre-trained models each specialise in a distinct weather pattern, they adapt efficiently to new sites and capture transferable, climate-dependent dynamics. Through the expert models' built-in calibration to seasonal and meteorological variability, we remove the industry-standard requirement of local measurements over a year. Our contributions are two-fold - we propose this novel framework and comprehensively evaluate it on eight offshore wind farms, achieving accurate cross-domain forecasting with under five months of site-specific data. Our experiments achieve a MAE of 3.52\%, providing empirical verification that reliable forecasts do not require a full annual cycle. Beyond power forecasting, this climate-aware transfer learning method opens new opportunities for offshore wind applications such as early-stage wind resource assessment, where reducing data requirements can significantly accelerate project development whilst effectively mitigating its inherent risks.