Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan
Wind power is crucial for achieving carbon neutrality, but its output can vary due to local wind conditions. The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impact on system operations. To assess this impact, the use of long-term reanalysis re...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-10-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2024.2374044 |
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| author | Yu Fujimoto Masamichi Ohba Yujiro Tanno Daisuke Nohara Yuki Kanno Akihisa Kaneko Yasuhiro Hayashi Yuki Itoda Wataru Wayama |
| author_facet | Yu Fujimoto Masamichi Ohba Yujiro Tanno Daisuke Nohara Yuki Kanno Akihisa Kaneko Yasuhiro Hayashi Yuki Itoda Wataru Wayama |
| author_sort | Yu Fujimoto |
| collection | DOAJ |
| description | Wind power is crucial for achieving carbon neutrality, but its output can vary due to local wind conditions. The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impact on system operations. To assess this impact, the use of long-term reanalysis results of wind data based on a numerical weather prediction (NWP) model is considered valid. However, in Japan, the behavior of on-shore wind power generation is influenced by diverse topographical and meteorological features (TMFs) of the installation site, making it challenging to assess possible operational impacts based solely on power curve-based estimates using a popular conversion equation. In this study, a nonparametric machine learning-based post-processing model that learns the statistical relationship between the TMFs at the target location and the actual wind farm (WF) output was developed to represent the expected per-unit output at each location. Focusing on historical reconstruction results and using this post-processing model to reproduce the real-world WF output behavior created a set of expected wind power generation profiles. The dataset includes hourly long term (1958–2012) wind power generation profiles expected under the WF installation assumptions at various on-shore locations in Japan with a 5 km spatial resolution and is expected to contribute to an accurate understanding of the impact of spatio-temporal wind power behavior. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.11496867 (Fujimoto et al., 2024). |
| format | Article |
| id | doaj-art-3cc3431c64a34601be0ce3ed812fe376 |
| institution | DOAJ |
| issn | 2096-4471 2574-5417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj-art-3cc3431c64a34601be0ce3ed812fe3762025-08-20T02:52:56ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172024-10-018473275410.1080/20964471.2024.2374044Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in JapanYu Fujimoto0Masamichi Ohba1Yujiro Tanno2Daisuke Nohara3Yuki Kanno4Akihisa Kaneko5Yasuhiro Hayashi6Yuki Itoda7Wataru Wayama8Advanced Collaborative Research Organization for Smart Society, Waseda University, Tokyo, JapanSustainable System Research Laboratory, Central Research Institute of Electric Power Industry, Abiko, Chiba, JapanDepartment of Advanced Science and Engineering, Waseda University, Tokyo, JapanSustainable System Research Laboratory, Central Research Institute of Electric Power Industry, Abiko, Chiba, JapanSustainable System Research Laboratory, Central Research Institute of Electric Power Industry, Abiko, Chiba, JapanDepartment of Advanced Science and Engineering, Waseda University, Tokyo, JapanDepartment of Advanced Science and Engineering, Waseda University, Tokyo, JapanPower System Engineering Department, Tohoku Electric Power Network Co., Inc., Sendai, Miyagi, JapanPower System Engineering Department, Tohoku Electric Power Network Co., Inc., Sendai, Miyagi, JapanWind power is crucial for achieving carbon neutrality, but its output can vary due to local wind conditions. The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impact on system operations. To assess this impact, the use of long-term reanalysis results of wind data based on a numerical weather prediction (NWP) model is considered valid. However, in Japan, the behavior of on-shore wind power generation is influenced by diverse topographical and meteorological features (TMFs) of the installation site, making it challenging to assess possible operational impacts based solely on power curve-based estimates using a popular conversion equation. In this study, a nonparametric machine learning-based post-processing model that learns the statistical relationship between the TMFs at the target location and the actual wind farm (WF) output was developed to represent the expected per-unit output at each location. Focusing on historical reconstruction results and using this post-processing model to reproduce the real-world WF output behavior created a set of expected wind power generation profiles. The dataset includes hourly long term (1958–2012) wind power generation profiles expected under the WF installation assumptions at various on-shore locations in Japan with a 5 km spatial resolution and is expected to contribute to an accurate understanding of the impact of spatio-temporal wind power behavior. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.11496867 (Fujimoto et al., 2024).https://www.tandfonline.com/doi/10.1080/20964471.2024.2374044On-shore wind powernumerical weather predictionmachine learningpost-processingdataset |
| spellingShingle | Yu Fujimoto Masamichi Ohba Yujiro Tanno Daisuke Nohara Yuki Kanno Akihisa Kaneko Yasuhiro Hayashi Yuki Itoda Wataru Wayama Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan Big Earth Data On-shore wind power numerical weather prediction machine learning post-processing dataset |
| title | Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan |
| title_full | Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan |
| title_fullStr | Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan |
| title_full_unstemmed | Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan |
| title_short | Historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio-temporal impact of on-shore wind in Japan |
| title_sort | historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing spatio temporal impact of on shore wind in japan |
| topic | On-shore wind power numerical weather prediction machine learning post-processing dataset |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2024.2374044 |
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