Prediction of global solar radiation in UAE using artificial neural networks

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

Research output: Contribution to conferencePaperpeer-review

21 Citations (Scopus)

Abstract

This paper presents an artificial neural network (ANN) model for predication global solar radiation (GSR) for main cities in the UAE namely, Abu Dhabi, Al-Ain and Dubai. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) techniques with comprehensive training algorithms, architectures, and different combinations of inputs are used to develop these models. The measured data include the maximum temperature (°C), mean wind speed (knot), sunshine hours, mean relative humidity (%) and mean daily global solar radiation on a horizontal surface (kWh/m2). This data was provided by the National Center of Meteorology and Seismology (NCMS) of Abu Dhabi. The results show the generalization capability of ANN approach and its ability to generate accurate prediction of GSR in UAE.

Original languageEnglish
Pages196-200
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 2nd International Conference on Renewable Energy Research and Applications, ICRERA 2013 - Madrid, Spain
Duration: Oct 20 2013Oct 23 2013

Other

Other2013 2nd International Conference on Renewable Energy Research and Applications, ICRERA 2013
Country/TerritorySpain
CityMadrid
Period10/20/1310/23/13

Keywords

  • Artificial Neural Networks
  • Global Solar Radiation (GSR)
  • Multilayer Perceptron
  • Radial Basis Function
  • UAE
  • modeling

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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