Performance Evaluation of Artificial Neural Networks in Estimating Global Solar Radiation, Case Study: New Borg El-arab City, Egypt
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.
Full text article
Generated from XML file
References
1. 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.
2. Wong LT, Chow WK. Solar radiation model. Appl Energy 2001;69:191–224.
3. 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.
1–8.
4. 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.
5. 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].
6. Angström A. Solar and terrestrial radiation. Q J R Meteorol Soc 1924;50:121–5.
7. Prescott JA. Evaporation from water surface in relation to solar radiation. Trans R Soc Aust
1940;46:114–8.
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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. Şenkal O, Kuleli T. Estimation of solar radiation over Turkey using artificial neural network and
satellite data. Appl Energy 2009;86:1222–8.
15. [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.
16. 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/agro.cgi?email=agroclim@larc.nasa.gov
(accessed April 10, 2015).
17. Fadare DA. Modelling of solar energy potential in Nigeria using an artificial neural network
model. Appl Energy 2009;86:1410–22.
18. Kalogirou SA. Artificial neural networks in renewable energy systems applications: a review.
Renew Sustain Energy Rev 2001;5:373–401.
19. Lin JT, Bhattacharyya D, Kecman V. Multiple regression and neural networks analyses in
composites machining. Compos Sci Technol 2003;63:539–48.
20. Picton PD. Neural networks. Palgrave; 2000.
21. 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.
22. 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.
23. Krenker A, Bešter J, Kos A. Introduction to the Artificial Neural Networks. Artif. Neural
Networks - Methodol. Adv. Biomed. Appl., 2011, p. 1046–54.
from satellite data in the tropics. Appl Energy 2009;86:1450–7.
2. Wong LT, Chow WK. Solar radiation model. Appl Energy 2001;69:191–224.
3. 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.
1–8.
4. 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.
5. 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].
6. Angström A. Solar and terrestrial radiation. Q J R Meteorol Soc 1924;50:121–5.
7. Prescott JA. Evaporation from water surface in relation to solar radiation. Trans R Soc Aust
1940;46:114–8.
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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. Şenkal O, Kuleli T. Estimation of solar radiation over Turkey using artificial neural network and
satellite data. Appl Energy 2009;86:1222–8.
15. [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.
16. 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/agro.cgi?email=agroclim@larc.nasa.gov
(accessed April 10, 2015).
17. Fadare DA. Modelling of solar energy potential in Nigeria using an artificial neural network
model. Appl Energy 2009;86:1410–22.
18. Kalogirou SA. Artificial neural networks in renewable energy systems applications: a review.
Renew Sustain Energy Rev 2001;5:373–401.
19. Lin JT, Bhattacharyya D, Kecman V. Multiple regression and neural networks analyses in
composites machining. Compos Sci Technol 2003;63:539–48.
20. Picton PD. Neural networks. Palgrave; 2000.
21. 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.
22. 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.
23. Krenker A, Bešter J, Kos A. Introduction to the Artificial Neural Networks. Artif. Neural
Networks - Methodol. Adv. Biomed. Appl., 2011, p. 1046–54.
Authors
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
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons Attribution 4.0 License or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution: other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
With the understanding that the above condition can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher's final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher's request, the Author agrees to furnish promptly to Publisher, at the Author's own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- The Work is the Author's original work;
- The Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- The Work is not pending review or under consideration by another publisher;
- The Work has not previously been published;
- The Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- The Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author's breach of the representations and warranties contained in Paragraph 7 above, as well as any claim or proceeding relating to Publisher's use and publication of any content contained in the Work, including third-party content.
This work is licensed under a Creative Commons Attribution 4.0 International License.