Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models

Hamdy K. Elminir, Yosry A. Azzam and Farag I. Younes

Energy, 2007, vol. 32, issue 8, 1513-1523

Abstract: For most of the locations all over Egypt the records of diffuse radiation in whatever scale are non-existent. In case that it exists, the quality of these records is not as good as it should be for most purposes and so an estimate of its values is desirable. To achieve such a task, an artificial neural network (ANN) model has been proposed to predict diffuse fraction (KD) in hourly and daily scale. A comparison between the performances of the ANN model with that of two linear regression models has been reported. An attempt was also done to describe the ANN outputs in terms of first order polynomials relating KD with clearness index (KT) and sunshine fraction (S/S0). If care is taken in considering the corresponding regional climatic differences, these correlations can be generalized and transferred to other sites. The results hint that the ANN model is more suitable to predict diffuse fraction in hourly and daily scales than the regression models in the plain areas of Egypt.

Keywords: Diffuse fraction; Linear regression model; Neural network; Back-propagation algorithm (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (42)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:32:y:2007:i:8:p:1513-1523

DOI: 10.1016/j.energy.2006.10.010

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