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PV power forecast using a nonparametric PV model
Almeida, Marcelo Pinho and Perpiñan Lamigueiro, Oscar and Narvarte Fernandez, Luis
PV power forecast using a nonparametric PV model.
"Solar Energy", v. 115
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%.
|FP7||308468||reduction, Reliability, Operational performance, Prediction and Simulation||Luis Narvarte||PhotoVoltaic Cost reduction, Reliability, Operational performance, Prediction and Simulation|
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