Abstract
In this study, temperature based artificial neural network (ANN) models and Angstrom type models for predicting global solar radiation were developed for selected locations in Nigeria.The ANN models were standard multi-layered feed forward, back-propagation neural networks trained with the Levenberg Marquardt algorithm using seventeen years data collected from Nigerian Meteorological Agency (NIMET), Abuja, Nigeria and tested with twenty-two years monthly averaged data downloaded from National Aeronautical Space Administration (NASA) online database. The network inputs were latitude, longitude, elevation, month, maximum ambient temperature (T max ) and minimum ambient temperature (T min ), while monthly average global solar radiation was the network output.The Angstrom type empirical models correlated global solar radiation with minimum and maximum ambient temperatures. The performance of the models were evaluated using statistical performance indicators, namely RMSE, MBE, R 2 and rank score. The coefficients of determination (R 2) of the ANN models were always greater than 99% for all the selected locations while the highest coefficient of determination for the empirical models was 89%. The temperature-based ANN models were thus shown to deliver superior and more reliable outcomes in comparison with the empirical models.
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References
Ezekwe, C.I., Ezeilo, C.O.: Measured Solar Radiation in a Nigerian Environment Compared with Predicted Data. Solar Energy 26, 181–186 (1982)
Aidan, J., Yadima, A., Ododo, J.C.: Modeling of Un-available Solar Radiation Using Some Climatological Parameters. Nigerian J Solar Energy 15(1), 18–26 (2005)
Augustine, C., Nnabuchi, M.N.: Correlation Between Sunshine Hours and Global Solar Radiation in Warri, Nigeria. The Pacific J. Sci. Tech. 10(4), 574–579 (2009)
Kassem, A., Aboukarima, A., El Ashmawy, N.: Development of Neural Network Model to Estimate Hourly Total and Diffuse Solar Radiation on Horizontal Surface at Alexandria City (Egypt). Journal of Applied Sciences Research 5(11), 2006–2016 (2009)
Falayi, E.O., Adepitan, J.O., Rabiu, A.B.: Empirical Models for the Correlation of Global Solar Radiation with Meteorological Data for Iseyin, Nigeria. International Journal of Physical Sciences 3(9), 210–216 (2008)
El-Sebaii, A., Trabea, A.: Estimation of Global Solar Radiation on Horizontal Surfaces Over Egypt. Egypt. J. Solids 28(1), 163–175 (2005)
Donatelli, M., Bellocchi, G., Fontana, F.: Software to Estimate Daily Radiation Data from Commonly Available Meteorological Variables. Agric. For. Meteorol. 18(3), 363–367 (2003)
Kalogirou, S.A.: Artificial Neural Networks in Renewable Energy Systems Application: A Review. Renewable Sustainable Energy Rev. 5, 373–401 (2001)
Knutti, R., Stocker, T.F., Joos, F., Plattner, G.K.: Probabilistic Climate Change Predictions Using Neural Networks. Climate Dynamics 21, 257–272 (2003)
Mohandes, M., Rehman, S., Halawani, T.O.: Estimation of Global Solar Radiation Using Artificial Neural Network. Renewable Energy 14(1), 79–84 (1998)
Reddy, K.S., Ranjan, M.: Solar Resource Estimation Using Artificial Neural Networks and Comparison with other Correlation Models. Energy Conversion and Management 44(15), 2519–2530 (2003)
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Pearson Education, Inc., New Jersey (2009)
Nissen, S.: Implementation of a Fast Artificial Neural Network. Department of Computer Science, University of Copenhagen, PDF Notes, 29 p. (2003)
Angstrom, A.: Solar and terrestrial radiation. Quart. Jour. Roy. Meteorol. Soc. 50, 121–125 (1924)
Prescott, J.A.: Evaporation from a water surface in relation to solar radiation. Trans. R. Soc. Sci. Austrialia 64, 114–125 (1940)
Garcia, J.V.: Principios Fisicos de la Climatolog ia, Ediciones. UNALM (Universidad Nacional Agraria La Molina: Lima, Peru) (1994)
Hargreaves, G., Samani, Z.: Estimating Potential Evaporation. Journal of Irrigation and Drainage Engineering 108, 225–230 (1982)
Okundamiya, M.S., Nzeako, A.N.: Empirical Model for Estimating Global Solar Radiation on Horizontal Surfaces for Selected Cities in the Six Geopolitical Zones in Nigeria. Research Journal of Applied Sciences, Engineering and Technology 2(8), 805–812 (2010)
Mubiru, J., Banda, E.J.K.B.: Estimation of Global Solar Radiation Using Artificial Neural Network. Solar Energy 82(2), 181–187 (2008)
Chukwu, S.C., Nwachukwu, A.N.: Analysis of Some Meteorological Parameters Using Artificial Neural Network Method for Markurdi, Nigeria. African Journal of Environmental Science and Technology 6(3), 182–188 (2012)
Abdulazeez, M.A.: Artificial Neural Network Estimation of Global Solar Radiation Using Meteorological Parameters in Gusau. Nigeria. Archiv. Appl. Sci. Res. 3(2), 586–595 (2011)
Egeonu, D.I., Njoku, H.O., Enibe, S.O.: Performance of Global Solar Radiation ANN Models with Different Input Parameter Combination. Submitted to: International Journal of Energy Research (2014)
Fadare, D.A.: Modelling of Solar Energy Potential in Nigeria Using an Artificial Neural Network Model. Applied Energy (2009), doi:10.1016/j.apenergy.2008.12.005
Duffie, J.A., Beckman, W.A.: Solar Engineering of Thermal Processes. Wiley & Sons Inc., New York (2006)
Howard, D., Beale, M.: Neural Network Toolbox for Use with MATLAB, User’s Guide, Version 4, pp. 133–205. The Math Works, Inc. Product (2000)
Martin, T.H., Howard, B.D.: Neural Network Design. PWS Publishing Company, United States of America (1996)
NASA: https://eosweb.larc.nasa.gov/ (accessed: December 10, 2012)
Mubiru, J., Banda, E.J.K.B., Ujanga, F.D., Senyonga, T.: Assessing the Performance of Global Solar Radiation Empirical Formulations in Kampala, Uganda. Theoretical and Applied Climatology 87(1-4), 179–181 (2007)
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Egeonu, D.I., Njoku, H.O., Okolo, P.N., Enibe, S.O. (2015). Comparative Assessment of Temperature Based ANN and Angstrom Type Models for Predicting Global Solar Radiation. In: Abraham, A., Krömer, P., Snasel, V. (eds) Afro-European Conference for Industrial Advancement. Advances in Intelligent Systems and Computing, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-319-13572-4_9
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DOI: https://doi.org/10.1007/978-3-319-13572-4_9
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