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PV power forecast using a nonparametric PV model

PV power forecast using a nonparametric PV model
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%.
- Universidade de Sâo Paulo Brazil
- Universidade de São Paulo (USP) Brazil
- UNIVERSIDADE DE SAO PAULO Brazil
- Universidade de Sao Paulo Brazil
- Universidad Politécnica de Madrid Spain
Informática, Matemáticas, Energías Renovables
Informática, Matemáticas, Energías Renovables
1 Research products, page 1 of 1
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