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

Abstract Climate change will impact wind and, therefore, wind power generation with largely unknown effects and magnitude. Climate models can provide insight 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 for 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 changes in yearly wind power generation. 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 the future wind is likely more reliable. Overall, our results indicate that wind energy will likely remain a reliable energy source.

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Publication Details

Journal
Environmental Data Science
Published
2025-01-01
DOI
https://doi.org/10.1017/eds.2025.10018
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Nina Effenberger, Nicole Ludwig
Environmental Data Science
Climate variability and models
article

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

Nina Effenberger, Nicole Ludwig
article en

Abstract

Abstract Climate change will impact wind and, therefore, wind power generation with largely unknown effects and magnitude. Climate models can provide insight 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 for 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 changes in yearly wind power generation. 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 the future wind is likely more reliable. Overall, our results indicate that wind energy will likely remain a reliable energy source.

Environmental Data ScienceVol. 4
University of Tübingen (DE)
Deutsche Forschungsgemeinschaft
Climate action
Openalex Percentile: Top 80%
Climate variability and models
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Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6 — Nina Effenberger, Nicole Ludwig · Environmental Data Science (2025) | TGRS Research Map | TGRS