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

Alberto Bocca, Lorenzo Bottaccioli, Eliodoro Chiavazzo, Matteo Fasano, Alberto Macii and Pietro Asinari

Renewable Energy, 2016, vol. 97, issue C, 457-467

Abstract: 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.

Keywords: Photovoltaic solar energy; Stochastic data models; Renewable energy; Fast energy potential assessment (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148116305134
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:97:y:2016:i:c:p:457-467

DOI: 10.1016/j.renene.2016.06.001

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().