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