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- SourceAvailable from: Haythem Ghazouani
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ABSTRACT: a b s t r a c t In this paper, an adaptive model for predicting hourly global, diffuse and direct solar irradiance is described. A dataset of measured air temperature, relative humidity, direct, diffuse and global horizontal irradiance for Jeddah site (Saudi Arabia) were used in this study. Several combinations have been pro-posed, and the best performance is obtained by using sunshine duration, air temperature and relative humidity as inputs of the developed adaptive a-model. A good agreement between measured and pre-dicted data is obtained. In fact, the correlation coefficient is more than 97% and the mean bias error is less than 0.8. A comparison between a Feed-Forward Neural Network (FFNN) and the adaptive proposed model is presented in order to demonstrate his performance.Energy Conversion and Management 01/2010; 51(4). DOI:10.1016/j.enconman.2009.10.034
- Ecole Nationale des Sciences de l'Informatique, 01/2010, Degree: PhD
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