%0 Journal Article
%@ 0038-092X
%A Almeida, Marcelo Pinho
%A PerpiÃ±an Lamigueiro, Oscar
%A Narvarte FernÃ¡ndez, Luis
%D 2015
%F upm:34853
%I Elsevier
%J Solar Energy
%K Numerical Weather Prediction; PV plant; PV power forecast; Quantile Regression; Random Forest; Weather Research and Forecasting
%P 354-368
%T PV power forecast using a nonparametric PV model
%U http://oa.upm.es/34853/
%V 115
%X 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%.