A review of solar energy modeling techniques

Research output: Contribution to journalArticle

147 Citations (Scopus)

Abstract

Solar radiation data provide information on how much of the sun's energy strikes a surface at a location on the earth during a particular time period. These data are needed for effective research in solar-energy utilization. Due to the cost of and difficulty in solar radiation measurements and these data are not readily available, alternative ways of generating these data are needed. In this paper, a review is made on the solar energy modeling techniques which are classified based on the nature of the modeling technique. Linear, nonlinear, artificial intelligence models for solar energy prediction have been considered in this review. The outcome of the review showed that the sunshine ratio, ambient temperature and relative humidity are the most correlated coefficients to solar energy.

Original languageEnglish
Pages (from-to)2864-2869
Number of pages6
JournalRenewable and Sustainable Energy Reviews
Volume16
Issue number5
DOIs
Publication statusPublished - Jun 2012

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Solar energy
Solar radiation
Sun
Artificial intelligence
Atmospheric humidity
Energy utilization
Earth (planet)
Costs
Temperature

Keywords

  • ANN
  • Modeling
  • Solar energy
  • Solar radiation

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Cite this

A review of solar energy modeling techniques. / Khatib, Tamer; Mohamed, Azah; Sopian, Kamaruzzaman.

In: Renewable and Sustainable Energy Reviews, Vol. 16, No. 5, 06.2012, p. 2864-2869.

Research output: Contribution to journalArticle

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