Solar radiation estimation using artificial neural networks

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 m...

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Bibliographic Details
Published inApplied energy Vol. 71; no. 4; pp. 307 - 319
Main Authors Dorvlo, Atsu S.S., Jervase, Joseph A., Al-Lawati, Ali
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.04.2002
Elsevier Science
Elsevier
SeriesApplied Energy
Subjects
Applied sciences
Artificial neural networks
Clearness index
Energy
Exact sciences and technology
Natural energy
Radial basis functions
Solar energy
Solar radiation
Solar radiation Radial basis functions Artificial neural networks Clearness index
Asia
Oman
Clearness index
Radial basis functions
Solar radiation
Artificial neural networks
Neural network
Root mean square value
Artificial intelligence
Modeling
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Summary: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.
ISSN:0306-2619
1872-9118
DOI:10.1016/S0306-2619(02)00016-8