8 Study on a day-ahead solar power forecast reducing serious overestimation with integrating multiple forecast data
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- TAKAMATSU Takahiro
- National Institute of Advanced Industrial Science and Technology
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- NAKAJIMA Kou
- National Institute of Advanced Industrial Science and Technology
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- OHTAKE Hideaki
- National Institute of Advanced Industrial Science and Technology
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- Oozeki Takashi
- National Institute of Advanced Industrial Science and Technology
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- YAMAGUCHI Koji
- Japan Weather Association
Bibliographic Information
- Other Title
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- 8 複数予報データを用いた翌日日射予報の大外し低減手法の検討
Abstract
<p>In power systems where large amounts of photovoltaic power generation have been installed, operational planning is based on next-day solar radiation forecasts. In order to realize efficient and stable power system operation, advanced next-day solar radiation forecasting is necessary, and forecasts must not only have average accuracy but also be able to control the risk of rare large outages. In this study, a multiple solar radiation forecast model using ensemble forecast data was constructed by machine learning, and a forecast model that can both improve the average accuracy and suppress the risk of large over-estimation was investigated.</p>
Journal
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- Proceedings of JSES conference
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Proceedings of JSES conference 2023 (0), 25-28, 2023-11-16
Japan Solar Energy Society
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Details 詳細情報について
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- CRID
- 1390580637985065728
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- ISSN
- 2758478X
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Allowed