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Use of Empirical Regression and Artificial Neural Network Models for Estimation of Global Solar Radiation in Dubai, UAE

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Causes, Impacts and Solutions to Global Warming

Abstract

The geographical location of the United Arab Emirates (UAE) (latitude between 26° and 32° North and longitude between 51° and 56° East) favors the development and utilization of solar energy. This chapter presents estimation models for the global solar radiation (GSR) in Dubai, UAE. It compares between six empirical regression models and the best of 11 different configurations of artificial neural network (ANN) models. The models have been developed using measured average daily GSR data for 7 years (2002–2008) while the measured data for the years 2009–2010 are used for testing the models. Results of monthly average daily GSR data of all the empirical models for the test period 2009–2010 yield low statistical error parameters and coefficients of determination (R2) better than 96 %. Comparison with ANN models and Solar Radiation (SoDa) Web site data shows that the optimal multilayer perceptron (MLP) ANN model is the best with R2 = 98 %, and with the lowest statistical error parameters. The results also confirm that a simple linear regression model provides a very good estimation for monthly and daily average GSR data.

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Abbreviations

xj :

Inputs of ANN

wij :

Weights of ANN

yj :

Outputs of ANN

f:

Activation function

netj :

Total weighted sum of input signals to neuron j

yj (t) :

Target output for neuron j

xcj :

Center of the radial basis function

Σ:

Summation function

ωs :

Mean sunrise hour angle in radians

ϕ:

Latitude in radians

δ:

Declination angle in radians

η:

Learning rate

σj :

A factor that depends on whether neuron j is an output/hidden neuron

μ:

Momentum coefficient

φj(x):

Hidden layer output (activation function) for RBF ANN

ANN:

Artificial Neural Network

G0 :

Extraterrestrial solar radiation on a horizontal surface (kWh/m2)

GIS:

Geographical Information System

GSR:

Mean daily Global Solar Radiation (kWh/m2)

MABE:

Mean Absolute Bias Error (kWh/m2)

MAPE:

Mean Absolute Percent Error

MBE:

Mean Bias Error (kWh/m2)

MLP:

Multilayer Perceptron

NASA:

National Aeronautics and Space Administration

R2 :

Coefficient of Determination

RBF:

Radial Basis Function

RGSR:

Clearness coefficient

RH:

Relative Humidity ( % )

RMSE:

Root-Mean-Square Error (kWh/m2)

RSSH:

Sunshine duration ratio

RREX:

Renewable energy Resource EXplorer

S0 :

Theoretical maximum sunshine hours

SoDa:

Solar radiation Data

SSE:

Solar meteorology and Solar Energy

SSH:

Mean daily Sunshine Hours

SWERA:

Solar and Wind Energy Resource Assessment

T:

Maximum air Temperature (degrees Celsius)

UAE:

United Arab Emirates

W:

Average Wind Speed (knots)

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Acknowledgments

The authors would like to thank the National Center for Meteorology and Seismology (NCMS), Abu Dhabi for providing the weather data.

Author information

Authors and Affiliations

  1. College of Engineering, United Arab Emirates University, 15551, Al-Ain, UAE

    Hassan A. N. Hejase & Maitha H. Al Shamisi

  2. Department of Electrical and Electronic Engineering, Lebanese International University, Beirut, Lebanon

    Ali H. Assi

Authors
  1. Hassan A. N. Hejase
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  2. Ali H. Assi
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  3. Maitha H. Al Shamisi
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Corresponding author

Correspondence to Hassan A. N. Hejase .

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario, Canada

    Ibrahim Dincer

  2. Makina Muhendisligi Bolumu, Dokuz Eylul University, Buca, Izmir, Turkey

    Can Ozgur Colpan

  3. Faculty of Civil Engineering, Istanbul Technical University, Maslak, Istanbul, Turkey

    Fethi Kadioglu

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Hejase, H.A.N., Assi, A.H., Shamisi, M.H.A. (2013). Use of Empirical Regression and Artificial Neural Network Models for Estimation of Global Solar Radiation in Dubai, UAE. In: Dincer, I., Colpan, C., Kadioglu, F. (eds) Causes, Impacts and Solutions to Global Warming. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7588-0_4

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