PV power forecast using a nonparametric PV model

Almeida, Marcelo Pinho and Perpiñan Lamigueiro, Oscar and Narvarte Fernández, Luis (2015). PV power forecast using a nonparametric PV model. "Solar Energy", v. 115 ; pp. 354-368. ISSN 0038-092X. https://doi.org/10.1016/j.solener.2015.03.006.


Title: PV power forecast using a nonparametric PV model
  • Almeida, Marcelo Pinho
  • Perpiñan Lamigueiro, Oscar
  • Narvarte Fernández, Luis
Item Type: Article
Título de Revista/Publicación: Solar Energy
Date: May 2015
ISSN: 0038-092X
Volume: 115
Freetext Keywords: Numerical Weather Prediction; PV plant; PV power forecast; Quantile Regression; Random Forest; Weather Research and Forecasting
Faculty: E.T.S.I. Diseño Industrial (UPM)
Department: Ingeniería Eléctrica, Electrónica Automática y Física Aplicada
Creative Commons Licenses: Recognition - Share

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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%.

Funding Projects

FP7308468PVCROPSLuis NarvartePhotoVoltaic Cost reduction, Reliability, Operational performance, Prediction and Simulation

More information

Item ID: 34853
DC Identifier: https://oa.upm.es/34853/
OAI Identifier: oai:oa.upm.es:34853
DOI: 10.1016/j.solener.2015.03.006
Official URL: http://www.sciencedirect.com/science/article/pii/S0038092X15001218
Deposited by: Oscar Perpiñán Lamigueiro
Deposited on: 06 Apr 2015 07:01
Last Modified: 30 May 2019 16:06
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