Performance Evaluation of Artificial Neural Networks in Estimating Global Solar Radiation, Case Study: New Borg El-Arab City, Egypt

Gasser E. Hassan (1), Mohamed A. Ali (2)
(1) Computer Based Engineering Applications Department, Informatics Research Institute-City for Scientific Research and Technological Applications, Egypt, Egypt,
(2) Computer Based Engineering Applications Department, Informatics Research Institute-City for Scientific Research and Technological Applications, Egypt, Egypt
https://doi.org/10.21625/essd.v2i1.73

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Issue
Vol. 2 Issue 1 (2017): Sustainable Development toward the Preservation of the Environment
Submitted
April 22, 2017
Published
June 30, 2017
Keywords:
    Artificial Neural Networks (ANN), Solar energy, Solar radiation models, Statistical indicators, Temperature-based models, Egypt
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Abstract

The most sustainable source of energy with unlimited reserves is the solar energy, which is the main source of all types of energy on earth. Accurate knowledge of solar radiation is considered to be the first step in solar energy availability assessment. It is also the primary input for various solar energy applications. The unavailability of the solar radiation measurements for several sites around the world leads to proposing different models for predicting the global solar radiation. Artificial neural network technique is considered to be an effective tool for modelling nonlinear systems and requires fewer input parameters. This work aims to investigate the performance of artificial neural network-based models in estimating global solar radiation. To achieve this goal, measured data set of global solar radiation for the case study location (Lat. 30˚ 51 ̀ N and long. 29˚ 34 ̀ E) are utilized for model establishment and validation. Mostly, common statistical indicators are employed for evaluating the performance of these models and recognizing the best model. The obtained results show that the artificial neural network models demonstrate promising performance in the prediction of global solar radiation. In addition, the proposed models provide superior consistency between the measured and estimated values.

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Authors

Gasser E. Hassan
Computer Based Engineering Applications Department, Informatics Research Institute-City for Scientific Research and Technological Applications, Egypt
[email protected] (Primary Contact)
Mohamed A. Ali
Computer Based Engineering Applications Department, Informatics Research Institute-City for Scientific Research and Technological Applications, Egypt
Author Biography

Mohamed A. Ali, Computer Based Engineering Applications Department, Informatics Research Institute-City for Scientific Research and Technological Applications, Egypt

Computer Based Engineering Applications Department, Informatics Research Institute-City for Scientific Research and Technological Applications

Hassan, G. E., & Ali, M. A. (2017). Performance Evaluation of Artificial Neural Networks in Estimating Global Solar Radiation, Case Study: New Borg El-Arab City, Egypt. Environmental Science & Sustainable Development, 2(1), 16–23. https://doi.org/10.21625/essd.v2i1.73
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Article Details

Received 2017-04-22
Accepted 2017-06-06
Published 2017-06-30