Technical Briefs

Estimation of Global Solar Radiation Using Artificial Neural Networks in Abu Dhabi City, United Arab Emirates

[+] Author and Article Information
Maitha Al-Shamisi, Hassan Hejase

Department of Electrical Engineering,
UAE University,
P. O. Box 15551,
Al-Ain, United Arab Emirates

Ali Assi

Department of Electrical and Electronic Engineering,
Lebanese International University,
P. O. Box 146404,
Mazraa, Beirut, Lebanon
e-mail: ali.assi@liu.edu.lb

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received April 12, 2012; final manuscript received October 20, 2013; published online November 26, 2013. Assoc. Editor: Philippe Blanc.

J. Sol. Energy Eng 136(2), 024502 (Nov 26, 2013) (5 pages) Paper No: SOL-12-1097; doi: 10.1115/1.4025826 History: Received April 12, 2012; Revised October 20, 2013

The geographical location (Latitude: 24 deg 28′ N and Longitude: 54 deg 22′ E) of Abu Dhabi city in the United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for the estimation of monthly mean global solar radiation (GSR) on a horizontal surface in Abu Dhabi. The ANN models are presented and implemented on a 16-yr measured meteorological data set for Abu Dhabi comprising the maximum daily temperature, mean daily wind speed, mean daily sunshine hours, and mean daily relative humidity between 1993 and 2008. The meteorological data between 1993 and 2003 are used for training the ANN and data between 2004 and 2008 are used for testing the estimated values. Multilayer perceptron (MLP) and radial basis function (RBF) are used as ANN learning algorithms. The results attest to the capability of ANN techniques and their ability to produce accurate estimation models.

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Assi, A., and Jama, M., 2010, “Estimating Global Solar Radiation on Horizontal From Sunshine Hours in Abu Dhabi–UAE. Advances in Energy Planning, Environmental Education and Renewable Energy Sources,” 4th WSEAS International Conference on Renewable Energy Sources, Sousse, Tunisia, May 3–6, pp. 101–108.
Mohandes, M., Rehman, S., and Halawani, T. O., 1998, “Estimation of Global Solar Radiation Using Artificial Neural Networks,” Renewable Energy, 14, pp. 179–184. [CrossRef]
Kassem, A. S., Aboukarima, A. M., and El Ashmawy, N. M., 2009, “Development of Neural Network Model to Estimate Hourly Total and Diffuse Solar Radiation on Horizontal Surface at Alexandria City (Egypt),” J. Appl. Sci. Res., 5(11), pp. 2006–2015, available from http://aensiweb.com/jasr/jasr/2009/2006-2015.pdf
Bulut, H., Büyükalaca, O., and Yılmaz, A., 2009, “Generation of Typical Solar Radiation Year for Mediterranean Region of Turkey,” Int. J. Green Energy, 6(2), pp. 173–183. [CrossRef]
Almorox, J., Benito, M., and Hontoria, C., 2008, “Estimation of Global Solar Radiation in Venezuela,” Interciencia, 33(4), pp. 280–283, available from http://www.scielo.org.ve/scielo.php?script=sci_arttext&pid=S0378-18442008000400009&lng=es&nrm=iso
Falayi, E. O., Adepitan, J. O., and Rabiu, A. B., 2008, “Empirical Models for the Correlation of Global Solar Radiation With Meteorological Data for Iseyin, Nigeria,” Int. J. Phys. Sci., 3(9), pp. 210–216, http://www.academicjournals.org/journal/IJPS/article-abstract/2C4539B14842
Fortin, J., Anctil, F., Parent, L., and Bolinder, M., 2008, “Comparison of Empirical Daily Surface Incoming Solar Radiation Models,” Agric. Forest Meteorol., 148, pp. 1332–1340. [CrossRef]
Akinoglu, G., and Ecevit, A., 1990, “A Further Comparison and Discussion of Sunshine Based Models to Estimate Global Solar Radiation,” Energy, 15, pp. 865–872. [CrossRef]
Samuel, T., 1991, “Estimation of Solar Radiation for Sri Lanka,” Sol. Energy, 47, pp. 333–337. [CrossRef]
Ampratwum, B., and Dorvlo, A. S. S., 1999, “Estimation of Solar Radiation From the Number of Sunshine Hours,” Appl. Energy, 63, pp. 161–167. [CrossRef]
Podestá, G., Núñez, L., Villanueva, C., and Skanski, M., 2004, “Estimating Daily Solar Radiation in the Argentine Pampas,” Agric. Forest Meteorol., 123, pp. 41–53. [CrossRef]
Angström, A., 1924, “Solar and Terrestrial Radiation,” Q. J. R. Meteorol. Soc., 50(210), pp. 121–125. [CrossRef]
Dorvlo, A. S. S., Jervase, J. A., and Al-Lawati, A., 2002, “Solar Radiation Estimation Using Artificial Neural Networks,” Appl. Energy, 71, pp. 307–319. [CrossRef]
Hontoria, L., Riesco, J., Zufiria, P., and Aguilera, J., 1999, “Improved Generation of Hourly Solar Radiation Artificial Series Using Neural Networks,” Fifth International Conference on Engineering Applications of Neural Networks (EANN'99), Warsaw, Poland, September 13–15, pp. 87–92.
Hontoria, L., Aguilera, J., and Zufiria, P., 2002, “Generation of Hourly Irradiation Synthetic Series Using the Neural Network Multilayer Perceptron,” Sol. Energy, 72, pp. 441–446. [CrossRef]
Krishnaiah, T., Srinivasa Rao, S., Madhumurthy, K., and Reddy, K. S., 2007, “A Neural Network Approach for Modelling Global Solar Radiation,” Appl. Sci. Res., 3(10), pp. 1105–1111, available from http://www.aensiweb.com/jasr/jasr/2007/1105-1111.pdf
Elizondo, D., Hoogenboom, G., and McClendon, R. W., 1994, “Development of a Neural Network Model to Predict Daily Solar Radiation,” Agric. Forest Meteorol., 71, pp. 115–132. [CrossRef]
Tymvios, F., Michaelides, S., and Skouteli, C., 2008, “Estimation of Surface Solar Radiation With Artificial Neural Networks,” Modeling Solar Radiation at the Earth Surface, Viorel Badescu, ed., Springer, Berlin, pp. 221–256.
Lam, J. C., Kevin, K. W., and Yang, L., 2008, “Solar Radiation Modelling Using ANNs for Different Climates in China,” Energy Convers. Manage., 49, pp. 1080–1090. [CrossRef]
Mubiru, J., 2008, “Predicting Total Solar Irradiation Values Using Artificial Neural Networks,” Renewable Energy, 33(10), pp. 2329–2332. [CrossRef]
Rehman, S., and Mohandes, M., 2008, “Artificial Neural Network Estimation of Global Solar Radiation Using Air Temperature and Relative Humidity,” Energy Policy, 36, pp. 571–576. [CrossRef]
Behrang, M. A., Assareh, E., Ghanbarzadeh, A., and Noghrehabadi, A. R., 2010, “The Potential of Different Artificial Neural Network (ANN) Techniques in Daily Global Solar Radiation Modeling Based on Meteorological Data,” Sol. Energy, 84, pp. 1468–1480. [CrossRef]
Mohandes, M., Balghonaim, A., Kassas, M., Rehman, S., and HalawaniT. O., 2000, “Use of Radial Basis Functions for Estimating Monthly Mean Daily Solar Radiation,” Sol. Energy, 68(2), pp. 161–168. [CrossRef]
Mubiru, J., and Banda, E. J. K. B., 2008, “Estimation of Monthly Average Daily Global Solar Irradiation Using Artificial Neural Networks,” Sol. Energy, 82, pp. 181–187. [CrossRef]
Kalogirou, S., Michaelides, S., and Tymvios, F., 2002, “Prediction of Maximum Solar Radiation Using Artificial Neural Networks,” World Renewable Energy Congress VII, Cologne, Germany, June 28-July 5.
Sozen, A., Arcaklioglu, E., Ozalp, M., and Kanit, E. G., 2005, “Forecasting Based On Neural Network Approach of Solar Potential in Turkey,” Renewable Energy, 30(7), pp. 1075–1090. [CrossRef]
Sozen, A., Arcaklioglu, E., Ozalp, M., and Kanit, E. G., 2005, “Solar-Energy Potential in Turkey,” Appl. Energy, 80, pp. 367–381. [CrossRef]
Sozen, A., Arcaklioglu, E., Ozalp, M., and Kanit, E. G., 2004, “Use of Artificial Neural-Networks for Mapping the Solar Potential in Turkey,” Appl. Energy, 77, pp. 273–286. [CrossRef]
Haykin, S., 2009, Neural Networks and Learning Machines, 3rd ed., Pearson Education, Inc., NJ.
Yang, J., Rivard, H., and Zmeureanu, R., 2005, “Building Energy Prediction With Adaptive Artificial Neural Networks,” Ninth International IBPSA Conference, Montréal, Canada, August 15–18.
Chantasut, N., Charoenjit, C., and Tanprasert, C., 2004, “Predictive Mining of Rainfall Predictions Using Artificial Neural Networks for Chao Phraya River,” 4th International Conference of the Asian Federation of Information Technology in Agriculture and the 2nd World Congress on Computers in Agriculture and Natural Resources, Bangkok, Thailand, August 9–12.
Al-Alawi, S. M., and Al-Hinai, A., 1998, “An ANN-Based Approach for Predicting Global Solar Radiation in Locations With No Measurements,” Renewable Energy, 14(1–4), pp. 199–204. [CrossRef]
Jayawardena, A. W., Achela, D., and Fernando, K., 1998, “Use of Radial Basis Function Type Artificial Neural Networks for Runoff Simulation,” Comput. Aided Civ. Infrastruct. Eng., 13, pp. 91–99. [CrossRef]
MathWorks, 2013, “Neural Network Toolbox,” MathWorks Inc., Natick, MA, http://www.mathworks.com/help/toolbox/nnet/newrb.html
Assi, A., Al-Shamisi, M., and Hejase, H., 2011, “Prediction of Global Solar Radiation in Abu Dhabi City—UAE,” 26th European Photovoltaic Solar Energy Conference and Exhibition, Hamburg, Germany, September 5–9, pp. 4328–4333.


Grahic Jump Location
Fig. 1

Geographical location of Abu Dhabi, UAE (north of UAE at latitude: 24 deg 28′ N and longitude: 54 deg 22′ E)

Grahic Jump Location
Fig. 2

Comparison between measured data and estimated MLP ANN models (1–11)

Grahic Jump Location
Fig. 3

Comparison between measured data and estimated RBF ANN models (1–11)

Grahic Jump Location
Fig. 4

Comparison of monthly mean daily GSR data from the optimal ANN and linear regression models with measured data in Abu Dhabi, UAE for years 2004–2008




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