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dc.contributor.authorde Jesus Euan Campos, Carolina
dc.contributor.authorSun, Ying
dc.contributor.authorReich, Brian J.
dc.date.accessioned2022-01-24T08:28:20Z
dc.date.available2022-01-24T08:28:20Z
dc.date.issued2022-01-20
dc.date.submitted2021-03-16
dc.identifier.citationEuán, C., Sun, Y., & Reich, B. J. (2022). Statistical analysis of multi-day solar irradiance using a threshold time series model. Environmetrics. doi:10.1002/env.2716
dc.identifier.issn1180-4009
dc.identifier.issn1099-095X
dc.identifier.doi10.1002/env.2716
dc.identifier.urihttp://hdl.handle.net/10754/675109
dc.description.abstractThe analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Recently, new statistical models have been proposed to provide stochastic simulations of high-resolution data to downscale and forecast solar irradiance measurements. Most of the existing models are linear and highly depend on normality assumptions. However, solar irradiance shows strong nonlinearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive model to quantify the variability of the daily irradiance time series. We establish the sufficient conditions for our model to be stationary, and we develop an inferential procedure to estimate the model parameters. When we apply our model to study the statistical properties of observed irradiance data in Guadeloupe island group, a French overseas region located in the Southern Caribbean Sea, we are able to characterize two states of the irradiance series. These states represent the clear-sky and non-clear sky regimes. Using our model, we are able to simulate irradiance series that behave similarly to the real data in mean and variability, and more accurate forecasts compared to linear models.
dc.description.sponsorshipThis publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No. OSR-2019-CRG7-3800.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1002/env.2716
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleStatistical analysis of multi-day solar irradiance using a threshold time series model
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalEnvironmetrics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Mathematics and Statistics Lancaster University Lancaster UK
dc.contributor.institutionDepartment of Statistics North Carolina State University Raleigh North Carolina USA
kaust.personSun, Ying
kaust.grant.numberOSR-2019-CRG7-3800
dc.date.accepted2022-01-02
dc.identifier.eid2-s2.0-85122917235
refterms.dateFOA2022-01-24T08:29:11Z
kaust.acknowledged.supportUnitCRG
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.