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.

翻译：雄心勃勃的脱碳目标正推动新建海上风电场订单的增长。为使这些新投产的电站顺利运行，需要从一开始就获得准确的功率预测。这有助于维持电网稳定、优化备用容量管理并实现高效能源交易。尽管机器学习模型具有优越性能，但它们通常需要大量场站特定数据，而新建风电场尚不具备此类数据。为克服数据稀缺问题，我们提出一种新颖的迁移学习框架，该框架依据协变量气象特征对功率输出进行聚类。我们并非训练单一通用模型，而是采用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注于特定的天气模式，它们能高效适应新场站并捕捉可迁移的气候相关动态特性。通过专家模型内建的季节与气象变率校准机制，我们消除了行业标准中对全年本地测量数据的要求。我们的贡献包括两方面——提出该创新框架并在八个海上风电场进行全面评估，仅使用不足五个月的场站特定数据即实现精确的跨域预测。实验结果显示平均绝对误差为3.52%，实证验证了可靠预测无需完整年周期数据。除功率预测外，这种气候感知的迁移学习方法为海上风电应用开辟了新机遇，例如早期风资源评估——降低数据需求可显著加速项目开发，同时有效缓解其固有风险。