PV power forecast using a nonparametric PV model

被引：164

作者：

Almeida, Marcelo Pinho ^{[1
]}

Perpinan, Oscar ^{[2
,3
]}

Narvarte, Luis ^{[3
]}

机构：

[1] Univ Sao Paulo, Inst Energia & Ambiente, Sao Paulo, Brazil

[2] UPM, Dept Elect Engn, ETSIDI, Madrid 28012, Spain

[3] Inst Energia Solar, Madrid, Spain

来源：

SOLAR ENERGY | 2015年 / 115卷

关键词：

PV plant; Numerical Weather Prediction; Weather Research and Forecasting; PV power forecast; Random Forest; Quantile Regression; MEAN-SQUARE ERROR; SOLAR; PREDICTION; OUTPUT; RMSE; MAE;

D O I：

10.1016/j.solener.2015.03.006

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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%. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：354 / 368

页数：15

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