Turbine location-aware multi-decadal wind power predictions using CMIP6

Climate change will impact wind and, therefore, wind power generation with largely unknown effect and magnitude. Climate models can provide insights and should be used for long-term power planning. In this work, we use Gaussian processes to predict power output given wind speeds from a global climate model. We validate the aggregated predictions from past climate model data with actual power generation, which supports using CMIP6 climate model data for multi-decadal wind power predictions and highlights the importance of being location-aware. We find that wind power projections of the two in-between climate scenarios, SSP2-4.5 and SSP3-7.0, closely align with actual wind power generation between 2015 and 2023. Our location-aware future predictions up to 2050 reveal only minor yearly wind power generation changes. Our analysis also reveals larger uncertainty associated with Germany's coastal areas in the North than Germany's South, motivating wind power expansion in regions where future wind is likely more reliable. Overall, our results indicate that wind energy will likely remain a reliable energy source. The methodology we present is adaptable to any other country or region with known wind farm locations and known historical aggregate power generation.