Submissions
Guidelines
Performance Evaluation of Artificial Neural Networks in Estimating Global Solar Radiation, Case Study: New Borg El-arab City, Egypt
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.
Keywords
Artificial Neural Networks (ANN); Solar energy; Solar radiation models; Statistical indicators; Temperature-based models; Egypt
References
Janjai S, Pankaew P, Laksanaboonsong J. A model for calculating hourly global solar radiation
from satellite data in the tropics. Appl Energy 2009;86:1450–7.
Wong LT, Chow WK. Solar radiation model. Appl Energy 2001;69:191–224.
Gasser E. Hassan, Mohamed A. Ali MEY. Solar Energy Availability in Suez Canal’s Zone - Case
study: Port Said and Suez cities, Egypt. Int. Marit. Transp. Logist. Conf. (MARLOG 6), 2017, p.
–8.
El-Sebaii a. a., Al-Hazmi FS, Al-Ghamdi a. a., Yaghmour SJ. Global, direct and diffuse solar
radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia. Appl Energy 2010;87:568–76.
Gasser E. Hassan, M. Elsayed Youssef, Mohamed A. Ali, Zahraa E. Mohamed AH. Evaluation of Different Sunshine-Based Models for Predicting Global Solar Radiation – Case Study: New Borg
El-Arab City, Egypt. Therm Sci 2017:[Accepted].
Angström A. Solar and terrestrial radiation. Q J R Meteorol Soc 1924;50:121–5.
Prescott JA. Evaporation from water surface in relation to solar radiation. Trans R Soc Aust
;46:114–8.
Besharat F, Dehghan A a., Faghih AR. Empirical models for estimating global solar radiation: A
review and case study. Renew Sustain Energy Rev 2013;21:798–821.
Almorox J, Benito M, Hontoria C. Estimation of monthly Angstrom-Prescott equation
coefficients from measured daily data in Toledo, Spain. Renew Energy 2005;30:931–6.
Youssef E, Hassan GE, Ali MA. Investigating the performance of different models in estimating
global solar radiation. Adv Nat Appl Sci 2016;10:379–89.
Hassan GE, Youssef ME, Mohamed ZE, Ali MA, Hanafy AA. New Temperature-based Models
for Predicting Global Solar Radiation. Appl Energy 2016;179:437–50.
Hassan GE, Youssef ME, Ali MA, Mohamed ZE, Shehata AI. Performance assessment of
different day-of-the-year-based models for estimating global solar radiation - Case study: Egypt. J
Atmos Solar-Terrestrial Phys 2016;149:69–80.
Jiang Y. Computation of monthly mean daily global solar radiation in China using artificial neural
networks and comparison with other empirical models. Energy 2009;34:1276–83.
Şenkal O, Kuleli T. Estimation of solar radiation over Turkey using artificial neural network and
satellite data. Appl Energy 2009;86:1222–8.
[15] Li H, Ma W, Lian Y, Wang X. Estimating daily global solar radiation by day of year in
China. Appl Energy 2010;87:3011–7.
NASA Data. NASA Surface meteorology and Solar Energy n.d. https://eosweb.larc.nasa.gov/cgibin/sse/daily.cgi
& https://power.larc.nasa.gov/cgi-bin/[email protected]
(accessed April 10, 2015).
Fadare DA. Modelling of solar energy potential in Nigeria using an artificial neural network
model. Appl Energy 2009;86:1410–22.
Kalogirou SA. Artificial neural networks in renewable energy systems applications: a review.
Renew Sustain Energy Rev 2001;5:373–401.
Lin JT, Bhattacharyya D, Kecman V. Multiple regression and neural networks analyses in
composites machining. Compos Sci Technol 2003;63:539–48.
Picton PD. Neural networks. Palgrave; 2000.
Rahimikhoob A. Estimating global solar radiation using artificial neural network and air
temperature data in a semi-arid environment. Renew Energy 2010;35:2131–5.
Ajayi OO, Ohijeagbon OD, Nwadialo CE, Olasope O. New model to estimate daily global solar
radiation over Nigeria. Sustain Energy Technol Assessments 2014;5:28–36.
Krenker A, Bešter J, Kos A. Introduction to the Artificial Neural Networks. Artif. Neural
Networks - Methodol. Adv. Biomed. Appl., 2011, p. 1046–54.
Copyright (c) 2017 Gasser E. Hassan
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Gasser E. Hassan
