Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6
This provides location-aware predictions for energy planners in Germany, though it is incremental as it applies existing methods to new climate model data.
The researchers tackled the problem of predicting long-term wind power generation in Germany under climate change by using Gaussian processes with CMIP6 climate model data, finding only minor changes in yearly wind power generation up to 2050 and that projections for two climate scenarios closely matched actual generation from 2015 to 2023.
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 and compare the aggregated predictions to actual power generation. Analyzing past climate model data supports the use of CMIP6 climate model data for multi-decadal wind power predictions and highlights the importance of being location-aware. Our predictions up to 2050 reveal only minor changes in yearly wind power generation. 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 analysis also reveals larger uncertainty associated with Germany's coastal areas in the North as compared to 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 in the future.