Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6
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 climat...
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Cambridge University Press
2025-01-01
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| Series: | Environmental Data Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460225100186/type/journal_article |
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| author | Nina Effenberger Nicole Ludwig |
| author_facet | Nina Effenberger Nicole Ludwig |
| author_sort | Nina Effenberger |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-60ab19c00a214d8b8d705b3c3d367211 |
| institution | Kabale University |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-60ab19c00a214d8b8d705b3c3d3672112025-08-20T04:02:28ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2025.10018Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6Nina Effenberger0https://orcid.org/0000-0002-0713-1164Nicole Ludwig1https://orcid.org/0000-0003-3230-8918Cluster of Excellence Machine Learning, University of Tübingen, Tübingen, GermanyCluster of Excellence Machine Learning, University of Tübingen, Tübingen, GermanyClimate 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.https://www.cambridge.org/core/product/identifier/S2634460225100186/type/journal_articleclimate changedownscalingwind powerwind turbines |
| spellingShingle | Nina Effenberger Nicole Ludwig Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6 Environmental Data Science climate change downscaling wind power wind turbines |
| title | Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6 |
| title_full | Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6 |
| title_fullStr | Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6 |
| title_full_unstemmed | Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6 |
| title_short | Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6 |
| title_sort | turbine location aware multi decadal wind power predictions for germany using cmip6 |
| topic | climate change downscaling wind power wind turbines |
| url | https://www.cambridge.org/core/product/identifier/S2634460225100186/type/journal_article |
| work_keys_str_mv | AT ninaeffenberger turbinelocationawaremultidecadalwindpowerpredictionsforgermanyusingcmip6 AT nicoleludwig turbinelocationawaremultidecadalwindpowerpredictionsforgermanyusingcmip6 |