Estimating photovoltaic energy potential from a minimal set of randomly sampled data

被引：10

作者：

Bocca, Alberto ^{[1
]}

Bottaccioli, Lorenzo ^{[1
]}

Chiavazzo, Eliodoro ^{[2
]}

Fasano, Matteo ^{[2
]}

Macii, Alberto ^{[1
]}

Asinari, Pietro ^{[2
]}

机构：

[1] Politecn Torino, Dipartimento Automat & Informat, Corso Duca Abruzzi 24, I-10129 Turin, Italy

[2] Politecn Torino, Dipartimento Energia, Corso Duca Abruzzi 24, I-10129 Turin, Italy

来源：

RENEWABLE ENERGY | 2016年 / 97卷

关键词：

Photovoltaic solar energy; Stochastic data models; Renewable energy; Fast energy potential assessment; SOLAR-RADIATION; MODEL; EUROPE; WIND;

D O I：

10.1016/j.renene.2016.06.001

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The remarkable rise of photovoltaics in the world over the past years testifies of the great improvement in the use of solar energy. Opportunities for further new PV installations are being sought, especially power plants in areas with as yet little exploited solar energy potential. In this paper, we describe a methodology for generating estimation models of PV electricity for installations in large regions where only a few scattered data or measurement stations are available. For validation only, application of this methodology was performed considering Italy, where estimations can be benchmarked using the Photovoltaic Geographical Information System (PVGIS) by the Joint Research Centre of the European Commission. The results show that the mean absolute errors were usually lower than 4%, compared to the PVGIS data, for about 90% of the estimates of PV electricity, and about 6% for the greatest mean error. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：457 / 467

页数：11

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