Descripción
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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%. ? 2015 Elsevier Ltd. All rights reserved. | |
Internacional
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Si |
JCR del ISI
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Si |
Título de la revista
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Solar Energy |
ISSN
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0038-092X |
Factor de impacto JCR
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3,541 |
Información de impacto
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Datos JCR del año 2013 |
Volumen
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115 |
DOI
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10.1016/j.solener.2015.03.006 |
Número de revista
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Desde la página
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354 |
Hasta la página
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368 |
Mes
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MARZO |
Ranking
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