Leveraging Seasonal Predictions to Anticipate Wind Energy Returns
Seasonal production forecasts are emerging as a helpful tool to anticipate wind energy fluctuations and improve planning. Our case study on Costa Rica’s 2024 wind drought illustrates how these forecasts can be turned into actionable insights, anticipating reduced power generation months in advance. Through the use of machine learning for model post-processing and the generation of confidence intervals, the forecast accurately predicted the significant wind production drop in the region six months ahead while providing a reliable measure of uncertainty. Ultimately, these results offer a realistic example of how long-range forecasting might benefit planning and resilience in wind energy operations.
By Yazmina Zurita Martel, R&D Data Scientist, Nebbo Weather, Spain
