Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters

2026-01-27

AI Summary

This study addresses the challenge of achieving high-accuracy power forecasting for newly commissioned offshore wind farms, which lack sufficient local historical data—a limitation that compromises grid stability and market operations. To overcome this, the authors propose a transfer learning framework based on meteorological clustering. The approach first clusters power output according to meteorological covariates and constructs an ensemble of expert models that effectively integrate dynamic climatic and seasonal characteristics. By doing so, it substantially reduces reliance on a full year of local data, thereby lowering the data requirements for model development. Experimental results across eight offshore wind farms demonstrate that the method achieves an average mean absolute error (MAE) of 3.52% using less than five months of local operational data, confirming its capability to deliver reliable forecasts even in the absence of complete annual records.

Technology Category

• Machine Learning: Ensemble Methods
• Search and Optimization: Metareasoning and Metaheuristics
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

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.

Problem

• offshore wind power forecasting
• data scarcity
• cross-domain forecasting
• transfer learning
• meteorological variability

Innovation

• transfer learning
• meteorological clustering
• cross-domain forecasting
• offshore wind power
• expert ensemble