PV power forecast using a nonparametric PV model
Data(s) |
01/05/2015
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Resumo |
Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that daily production is predicted with an absolute cvMBE lower than 1.3%. |
Formato |
application/pdf |
Identificador | |
Idioma(s) |
eng |
Publicador |
E.T.S.I. Diseño Industrial (UPM) |
Relação |
http://oa.upm.es/34853/1/Pinho_Perpinan_ea2014.pdf http://www.sciencedirect.com/science/article/pii/S0038092X15001218 info:eu-repo/grantAgreement/EC/FP7/308468 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.solener.2015.03.006 |
Direitos |
http://creativecommons.org/licenses/by-sa/3.0/es/ info:eu-repo/semantics/openAccess |
Fonte |
Solar Energy, ISSN 0038-092X, 2015-05, Vol. 115 |
Palavras-Chave | #Informática #Matemáticas #Energías Renovables |
Tipo |
info:eu-repo/semantics/article Artículo NonPeerReviewed |