Long Term Wind Power Assessment based on Wind Speed Downscaling

Accurate assessment of long-term wind power potential requires high-resolution wind speed data, yet most climate projections operate at spatial resolutions too coarse for reliable local estimates. Deep learning methods developed for image super-resolution offer a promising solution by learning statistical mappings from coarse to fine-resolution wind fields. In this paper, we systematically compare four state-of-the-art deep learning approaches namely purely convolutional, attention-based, spatio-temporal, and diffusion-based across two wind speed downscaling tasks of increasing complexity, evaluating their performance not only on standard pixel-based metrics but also on physically informed and distributional metrics directly relevant to wind power estimation. We show that pixel-based metrics alone are insufficient for model selection in energy applications: models that appear comparable on reconstruction accuracy can differ by up to 15 percentage points in recovered cumulative wind power. Diffusion-based downscaling consistently outperforms competing approaches on physical and distributional metrics and most accurately recovers long-term wind power in both experimental settings, with the advantage becoming more pronounced on the more challenging cross-dataset task.