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

TL;DR

This study uses climate model data and Gaussian processes to predict long-term wind power generation in Germany, highlighting regional uncertainties and the reliability of wind energy amid climate change.

Contribution

It demonstrates the effectiveness of CMIP6 climate data and location-aware modeling for multi-decadal wind power predictions in Germany.

Findings

Minor changes in annual wind power projected up to 2050

Predictions for SSP2-4.5 and SSP3-7.0 align with recent data

Higher uncertainty in Germany's northern coastal regions

Abstract

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.