Artificial neural networks for predicting global solar radiation in Al Ain City - UAE

Maitha H. Al-Shamisi, Ali H. Assi, Hassan A.N. Hejase

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


The geographical location (latitude: 24° 16′ N and longitude: 55° 36′ E) of Al Ain city in the southwest of United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for predicting monthly global solar radiation (MGSR) on a horizontal surface in Al Ain. The ANN models are presented and implemented on 13-year measured meteorological data for Al Ain such as maximum temperature, mean wind speed, sunshine, and mean relative humidity between 1995 and 2007. The meteorological data between 1995 and 2004 are used for training the ANN and data between 2004 and 2007 are used for testing the predicted values. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks are used for the modeling. Models for the MGSR were obtained using eleven combinations of data sets based on the above mentioned measured data for Al Ain city. Forecasting performance parameters such as root mean square error (RMSE), mean bias error (MBE), mean absolute percentage error (MAPE), and correlation coefficient (R2) are presented for the model. The values of RMSE, MBE, MAPE, and R2 are found to be, respectively, 35%, 0.307%, 3.88%, and 92%. A comparison of estimated MGSR with regression models is carried out. The ANN model predicts better than other models. The estimated MGSR data are in reasonable agreement with the actual values. The results indicate the capability of the ANN technique over unseen data and its ability to produce accurate prediction models.

Original languageEnglish
Pages (from-to)443-456
Number of pages14
JournalInternational Journal of Green Energy
Issue number5
Publication statusPublished - May 28 2013


  • Artificial neural networks
  • Global solar radiation
  • Modeling
  • Multilayer perceptron
  • Prediction
  • Radial basis function

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment


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