Solar radiation estimation using aritificial neural networks

Atsu S.S. Dorvlo, Joseph A. Jervase, Ali Al-Lawati

Research output: Contribution to journalArticle

198 Citations (Scopus)

Abstract

Artificial Neural Network Methods are discussed for estimating solar radiation by first estimating the clearness index. Radial Basis Functions, RBF, and Multilayer Perceptron, MLP, models have been investigated using long-term data from eight stations in Oman. It is shown that both the RBF and MLP models performed well based on the root-mean-square error between the observed and estimated solar radiations. However, the RBF models are preferred since they require less computing power. The RBF model, obtained by training with data from the meteorological stations at Masirah, Salalah, Seeb, Sur, Fahud and Sohar, and testing with those from Buraimi and Marmul, was the best. This model can be used to estimate the solar radiation at any location in Oman.

Original languageEnglish
Pages (from-to)307-319
Number of pages13
JournalApplied Energy
Volume71
Issue number4
DOIs
Publication statusPublished - 2002

Keywords

  • Artificial neural networks
  • Clearness index
  • Radial basis functions
  • Solar radiation

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Energy(all)

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