Ambitious decarbonisation targets are rapidly increasing the commission 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. 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.

翻译：雄心勃勃的脱碳目标正推动新建海上风电场的快速投产。为保障这些新投产电站的运行，需从初始阶段即实现精准功率预测，这对电网稳定性、备用容量管理及高效能源交易至关重要。尽管机器学习模型表现优异，但其通常需要大量特定场址数据，而新建风电场尚不具备此条件。为克服数据稀缺问题，我们提出了一种新型迁移学习框架——根据协变量气象特征对功率输出进行聚类。该方法并非训练单一通用模型，而是通过专家模型集成进行预测，每个模型均在特定聚类上训练。由于这些预训练模型各自专精于不同天气模式，它们能高效适应新场址并捕获可迁移的、与气候相关的动态特性。本文贡献体现在两方面：既提出这一新型框架，又在八个海上风电场开展全面评估，证实仅需不足五个月的场址特定数据即可实现精准跨域预测。实验取得3.52%的平均绝对误差，实证表明可靠预测无需完整年度周期。除功率预测外，这种气候感知迁移学习方法为海上风电应用开辟新机遇，例如在早期风资源评估中，降低数据需求可显著加速项目开发，同时有效规避固有风险。