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Descripción
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| In this paper, a methodology using a nonparametric model is used to forecast AC power output of PV plants using as inputs several forecasts of meteorological variables from a Numerical Weather Prediction (NWP) 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 the AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that the daily production of individual plants can be predicted with a skill score up to 0.361. | |
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Internacional
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Si |
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Nombre congreso
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31st European Photovoltaic Solar Energy Conference |
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Tipo de participación
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960 |
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Lugar del congreso
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Hamburgo (Alemania) |
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Revisores
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Si |
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ISBN o ISSN
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3-936338-39-6 |
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DOI
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10.4229/EUPVSEC20152015-5BV.2.18 |
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Fecha inicio congreso
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14/09/2015 |
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Fecha fin congreso
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18/09/2015 |
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Desde la página
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2227 |
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Hasta la página
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2233 |
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Título de las actas
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Proceedings of the 31st PVSEC |