Research Article
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Year 2022, Volume 2, Issue 1, 27 - 40, 30.04.2022

Abstract

References

  • [1] Höök, M. and Tang, X., 2013. Depletion of fossil fuels and anthropogenic climate change—A review. Energy Policy,52, pp.797-809.
  • [2] Shahzad, U., 2015 The need for renewable energy sources.” International Journal of Information Technology and Electrical Engineering, 4, pp.16-18.
  • [3] Panwar, N., Kaushik, S. and Kothari, S., 2011. Role of renewable energy sources in environmental protection: A review. Renewable and Sustainable Energy Reviews, 15(3), pp.1513-1524.
  • [4] Singh, D., Sharma, N. K., Sood, Y. R., & Jarial, R. K. (2011). Global status of renewable energy and market: future prospectus and target. International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011), pp.171-176.
  • [5] Sharma, V. and Chandel, S., 2013. Performance and degradation analysis for long term reliability of solar photovoltaic systems: A review. Renewable and Sustainable Energy Reviews, 27, pp.753-767.
  • [6] Sirisamphanwong, C. and Ketjoy, N., 2012. Impact of spectral irradiance distribution on the outdoor performance of photovoltaic system under Thai climatic conditions. Renewable Energy, 38(1), pp.69-74.
  • [7] Erdem, Z., “Maksimum güç izleyicisi tasarımı”. Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Sakarya Üniversitesi,Sakarya, TÜRKİYE, 2009 Available at <https://acikbilim.yok.gov.tr/bitstream/handle/20.500.12812/455577/yokAcikBilim_346429.pdf> [Accessed 20 March 2022]
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  • [10] Meral, M. and Dinçer, F., 2011. A review of the factors affecting operation and efficiency of photovoltaic based electricity generation systems. Renewable and Sustainable Energy Reviews, 15(5), pp.2176-2184.
  • [11] Son, N. and Jung, M., 2020. Analysis of meteorological factor multivariate models for medium- and long-term photovoltaic solar power forecasting using long short-term memory. Applied Sciences, 11(1), p.316.
  • [12] Yenilenebilir enerji kaynakları - Güneş., [online] Available at: <https://enerji.gov.tr/eigm-yenilenebilir-enerji-kaynaklargunes> [Accessed 20 March 2022].
  • [13] Özgürel, M., Kılıç, M., 2003. İzmir için geleceğe yönelik yağış olasılıklarının markov zinciri modeliyle belirlenmesi. Ege Üniversitesi Ziraat Fakültesi Dergisi, 40(3):105-112.
  • [14] 2nd Regional Directorate of Meteorology – İzmir. [online] Available at: <https://izmir.mgm.gov.tr/gozlemsebekesi.aspx> [Accessed 19 March 2022]
  • [15] Almeida, J., 2002. Predictive non-linear modeling of complex data by artificial neural networks. Current Opinion in Biotechnology, 13(1), pp.72-76.
  • [16] Sharma, V., Rai, S., Dev, A., 2012. A Comprehensive Study of Artificial Neural Networks. International Journal of Advanced Research in Computer Science and Software Engineering, 2(10) pp: 278-284
  • [17] Jain, A., Jianchang Mao and Mohiuddin, K., 1996. Artificial neural networks: a tutorial. Computer, 29(3), pp.31-44.
  • [18] Wu, Y. and Feng, J., 2017. Development and application of artificial neural network. Wireless Personal Communications, 102(2), pp.1645-1656.
  • [19] Uzair, M., Jamil, N. (2020). Effects of hidden layers on the efficiency of neural networks. 2020 IEEE 23rd International Multitopic Conference (INMIC). [online] Available at: <https://ieeexplore.ieee.org/ielx7/9318042/9318043/09318195.pdf> [Accessed 21 March 2022]
  • [20] Kim, D., 1999. Normalization methods for input and output vectors in backpropagation neural networks. International Journal of Computer Mathematics, 71(2), pp.161-171.
  • [21] Sola, J. and Sevilla, J., 1997. Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on Nuclear Science, 44(3), pp.1464-1468.
  • [22] Jayalakshmi, T., Santhakumaran, A., 2011. Statistical normalization and backpropagation for classification. International Journal of Computer Theory and Engineering, pp.89-93.
  • [23] Dongare, A.D., Kharde, R.R., Kachare A.D., 2012. Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT) 2(1), pp.189-194.
  • [24] Sazlı, M.H., 2006. A brief review of feed-forward neural networks. Communications Faculty of Science University of Ankara, 50(1), pp.11-17.
  • [25] Erb, R.J., 1993. Introduction to backpropagation neural network computation. Pharmaceutical Research, 10(2), pp.165-170.
  • [26] Kalınlı, A., 2002. Elman ağının simulated annealing algoritması kullanarak sistem kimliklendirme için eğitilmesi. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 15(2), pp. 25-42.

Estimating Energy Production of Solar Power Plant at the University of Bakırçay Using Artificial Neural Networks Based on Meteorological Conditions

Year 2022, Volume 2, Issue 1, 27 - 40, 30.04.2022

Abstract

The rapid depletion of fossil fuels and environmental concerns have led people to work on renewable energy sources. In order to leave a cleaner and more liveable world for future generations and enable developed countries to produce more economical energy using their own resources, major investments have been made in renewable energy resources. Photovoltaic (PV) energy has a large share among renewable energy sources. Turkey has taken its place among the countries that are aware of the PV energy potential and invest in this field. The ratio of installed PV energy power to total installed power is also increased day by day in Turkey. However, meteorological factors affecting PV energy production make it difficult to compute energy production in advance. In this study, the relationship between meteorological data and power generation data was analyzed using the power generation data of the solar power plant (SPP) with an installed power of 400 kW in the student car park of the University of Bakırçay and the meteorological data of the province of İzmir. As a result of the comparison of the tests, energy production with respect to meteorological factors achieve a remarkable success rate with 95.3% when artificial neural networks are employed.

References

  • [1] Höök, M. and Tang, X., 2013. Depletion of fossil fuels and anthropogenic climate change—A review. Energy Policy,52, pp.797-809.
  • [2] Shahzad, U., 2015 The need for renewable energy sources.” International Journal of Information Technology and Electrical Engineering, 4, pp.16-18.
  • [3] Panwar, N., Kaushik, S. and Kothari, S., 2011. Role of renewable energy sources in environmental protection: A review. Renewable and Sustainable Energy Reviews, 15(3), pp.1513-1524.
  • [4] Singh, D., Sharma, N. K., Sood, Y. R., & Jarial, R. K. (2011). Global status of renewable energy and market: future prospectus and target. International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011), pp.171-176.
  • [5] Sharma, V. and Chandel, S., 2013. Performance and degradation analysis for long term reliability of solar photovoltaic systems: A review. Renewable and Sustainable Energy Reviews, 27, pp.753-767.
  • [6] Sirisamphanwong, C. and Ketjoy, N., 2012. Impact of spectral irradiance distribution on the outdoor performance of photovoltaic system under Thai climatic conditions. Renewable Energy, 38(1), pp.69-74.
  • [7] Erdem, Z., “Maksimum güç izleyicisi tasarımı”. Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Sakarya Üniversitesi,Sakarya, TÜRKİYE, 2009 Available at <https://acikbilim.yok.gov.tr/bitstream/handle/20.500.12812/455577/yokAcikBilim_346429.pdf> [Accessed 20 March 2022]
  • [8] The United Nations Environment Programme (UNEP), 2019. Global Trends in Renewable Energy Investment 2019 Report. [online] Available at: <https://www.unep.org/resources/report/global-trends-renewable-energy-investment- 2019> [Accessed 20 March 2022]. [9] TEİAŞ, 2022. Installed power report. [online] TEİAŞ. Available at: <https://www.teias.gov.tr/tr-TR/kurulu-guc-raporlari> [Accessed 20 March 2022].
  • [10] Meral, M. and Dinçer, F., 2011. A review of the factors affecting operation and efficiency of photovoltaic based electricity generation systems. Renewable and Sustainable Energy Reviews, 15(5), pp.2176-2184.
  • [11] Son, N. and Jung, M., 2020. Analysis of meteorological factor multivariate models for medium- and long-term photovoltaic solar power forecasting using long short-term memory. Applied Sciences, 11(1), p.316.
  • [12] Yenilenebilir enerji kaynakları - Güneş., [online] Available at: <https://enerji.gov.tr/eigm-yenilenebilir-enerji-kaynaklargunes> [Accessed 20 March 2022].
  • [13] Özgürel, M., Kılıç, M., 2003. İzmir için geleceğe yönelik yağış olasılıklarının markov zinciri modeliyle belirlenmesi. Ege Üniversitesi Ziraat Fakültesi Dergisi, 40(3):105-112.
  • [14] 2nd Regional Directorate of Meteorology – İzmir. [online] Available at: <https://izmir.mgm.gov.tr/gozlemsebekesi.aspx> [Accessed 19 March 2022]
  • [15] Almeida, J., 2002. Predictive non-linear modeling of complex data by artificial neural networks. Current Opinion in Biotechnology, 13(1), pp.72-76.
  • [16] Sharma, V., Rai, S., Dev, A., 2012. A Comprehensive Study of Artificial Neural Networks. International Journal of Advanced Research in Computer Science and Software Engineering, 2(10) pp: 278-284
  • [17] Jain, A., Jianchang Mao and Mohiuddin, K., 1996. Artificial neural networks: a tutorial. Computer, 29(3), pp.31-44.
  • [18] Wu, Y. and Feng, J., 2017. Development and application of artificial neural network. Wireless Personal Communications, 102(2), pp.1645-1656.
  • [19] Uzair, M., Jamil, N. (2020). Effects of hidden layers on the efficiency of neural networks. 2020 IEEE 23rd International Multitopic Conference (INMIC). [online] Available at: <https://ieeexplore.ieee.org/ielx7/9318042/9318043/09318195.pdf> [Accessed 21 March 2022]
  • [20] Kim, D., 1999. Normalization methods for input and output vectors in backpropagation neural networks. International Journal of Computer Mathematics, 71(2), pp.161-171.
  • [21] Sola, J. and Sevilla, J., 1997. Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on Nuclear Science, 44(3), pp.1464-1468.
  • [22] Jayalakshmi, T., Santhakumaran, A., 2011. Statistical normalization and backpropagation for classification. International Journal of Computer Theory and Engineering, pp.89-93.
  • [23] Dongare, A.D., Kharde, R.R., Kachare A.D., 2012. Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT) 2(1), pp.189-194.
  • [24] Sazlı, M.H., 2006. A brief review of feed-forward neural networks. Communications Faculty of Science University of Ankara, 50(1), pp.11-17.
  • [25] Erb, R.J., 1993. Introduction to backpropagation neural network computation. Pharmaceutical Research, 10(2), pp.165-170.
  • [26] Kalınlı, A., 2002. Elman ağının simulated annealing algoritması kullanarak sistem kimliklendirme için eğitilmesi. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 15(2), pp. 25-42.

Details

Primary Language English
Subjects Engineering, Basic Sciences
Journal Section Research Articles
Authors

Özgün UZ> (Primary Author)
IZMIR BAKIRCAY UNIVERSITY
0000-0002-6752-2861
Türkiye


Tuğba ÖZDEMİR This is me
DUZCE UNIVERSITY
0000-0002-5606-4105
Türkiye


Özge TÜZÜN ÖZMEN This is me
IZMIR BAKIRCAY UNIVERSITY
0000-0002-5204-3737
Türkiye

Publication Date April 30, 2022
Published in Issue Year 2022, Volume 2, Issue 1

Cite

APA Uz, Ö. , Özdemir, T. & Tüzün Özmen, Ö. (2022). Estimating Energy Production of Solar Power Plant at the University of Bakırçay Using Artificial Neural Networks Based on Meteorological Conditions . Artificial Intelligence Theory and Applications , 2 (1) , 27-40 . Retrieved from https://dergipark.org.tr/en/pub/aita/issue/70443/1136217