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Modelling of solar energy potential in Nigeria using an artificial neural network model

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  • Fadare, D.A.

Abstract

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14°N, log. 2-15°E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.

Suggested Citation

  • Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:9:p:1410-1422
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    References listed on IDEAS

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    1. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
    2. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2004. "Use of artificial neural networks for mapping of solar potential in Turkey," Applied Energy, Elsevier, vol. 77(3), pages 273-286, March.
    3. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    4. Kalogirou, Soteris A. & Neocleous, Constantinos C. & Schizas, Christos N., 1998. "Artificial neural networks for modelling the starting-up of a solar steam-generator," Applied Energy, Elsevier, vol. 60(2), pages 89-100, June.
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