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- W4310282760 abstract "United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world." @default.
- W4310282760 created "2022-11-30" @default.
- W4310282760 creator A5054096712 @default.
- W4310282760 date "2021-10-01" @default.
- W4310282760 modified "2023-09-26" @default.
- W4310282760 title "Estimating Weather Impacts On Utility-Scale Photovoltaic Plant Performance." @default.
- W4310282760 doi "https://doi.org/10.2172/1896360" @default.
- W4310282760 hasPublicationYear "2021" @default.
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