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• W3027162619 abstract "Abstract This paper illustrates the potential for seasonal prediction of wind and solar energy resources through a case study in the Yangtze River estuary. Sea surface temperature and geopotential height-based climate predictors, each with high correlation to ensuing seasonal wind speed and solar radiation at the Baoshan weather observing station, are identified and used to build statistical models to predict seasonal wind speed and solar radiation. Leave-one-out-cross-validation is applied to verify the predictive skill of the best performing candidate model for each season. We find that predictive skill is highest for both wind speed and solar radiation during winter, and lowest during summer. Specifically, we find the most skill when using climate information from the July-September season to predict wind speed or solar radiation during the subsequent November-January season. The ability to predict wind and solar energy availability in the upcoming season can help energy system planners and operators anticipate seasonal surpluses or shortfalls and take precautionary actions." @default.
• W3027162619 created "2020-05-29" @default.
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• W3027162619 date "2020-05-25" @default.
• W3027162619 modified "2023-10-13" @default.
• W3027162619 title "Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study" @default.
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• W3027162619 doi "https://doi.org/10.1038/s41598-020-65281-w" @default.
• W3027162619 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7248103" @default.
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