Prediction of global solar radiation in UAE using artificial neural networks

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

doi:10.1109/ICRERA.2013.6749750

Prediction of global solar radiation in UAE using artificial neural networks

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

Department of Electrical and Communication Engineering

Research output: Contribution to conference › Paper › peer-review

27 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 language	English
Pages	196-200
Number of pages	5
DOIs	https://doi.org/10.1109/ICRERA.2013.6749750
Publication status	Published - 2013
Event	2013 2nd International Conference on Renewable Energy Research and Applications, ICRERA 2013 - Madrid, Spain Duration: Oct 20 2013 → Oct 23 2013

Other

Other	2013 2nd International Conference on Renewable Energy Research and Applications, ICRERA 2013
Country/Territory	Spain
City	Madrid
Period	10/20/13 → 10/23/13

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

Access to Document

10.1109/ICRERA.2013.6749750

Cite this

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title = "Prediction of global solar radiation in UAE using artificial neural networks",

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.",

keywords = "Artificial Neural Networks, Global Solar Radiation (GSR), Multilayer Perceptron, Radial Basis Function, UAE, modeling",

author = "Assi, {Ali H.} and Al-Shamisi, {Maitha H.} and Hejase, {Hassan A.N.} and Ahmad Haddad",

year = "2013",

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note = "2013 2nd International Conference on Renewable Energy Research and Applications, ICRERA 2013 ; Conference date: 20-10-2013 Through 23-10-2013",

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AU - Assi, Ali H.

AU - Al-Shamisi, Maitha H.

AU - Hejase, Hassan A.N.

AU - Haddad, Ahmad

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Artificial Neural Networks

KW - Global Solar Radiation (GSR)

KW - Multilayer Perceptron

KW - Radial Basis Function

KW - UAE

KW - modeling

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T2 - 2013 2nd International Conference on Renewable Energy Research and Applications, ICRERA 2013

Y2 - 20 October 2013 through 23 October 2013

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Prediction of global solar radiation in UAE using artificial neural networks

Abstract

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Keywords

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