Comparison of Success Rates of Artificial Intelligence and Classical Methods in Estimation of Photovoltaic Energy Production: Study of İzmir Bakırçay University
Our aim was to create a reliable feasibility environment.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:Global solar energy has become a popular investment choice for investors, with installed power reaching 940GW according to 2021 data.
📊
Key Findings & Data:In our energy production estimation studies conducted at İzmir Bakırçay University SPP, we achieved a 70.24% success rate using classical estimation algorithms based on past production and meteorological data.
💡
Conclusion & Impact:Our aim was to create a reliable feasibility environment.
🔬Design:Research Article
📍Scope:2021 Verileri
⚙️Variables:photovoltaic energy · energy estimation methods · artificial neural networks
Abstract
Global solar energy has become a popular investment choice for investors, with installed power reaching 940GW according to 2021 data. Investors are interested in profit margin estimations based on energy production, which are provided through feasibility studies conducted before building solar power plants (SPP). While classical mathematical algorithms are typically used to calculate energy production, advances in technology offer opportunities to achieve better results. In our energy production estimation studies conducted at İzmir Bakırçay University SPP, we achieved a 70.24% success rate using classical estimation algorithms based on past production and meteorological data. However, by developing an artificial neural network, we achieved a 98.23% success rate, making it a more beneficial option for investors. Our aim was to create a reliable feasibility environment.
UZ, Ö., ÖZMEN, Ö. T. (2023). Comparison of Success Rates of Artificial Intelligence and Classical Methods in Estimation of Photovoltaic Energy Production: Study of İzmir Bakırçay University. Artificial Intelligence Theory and Applications, Cilt: 3 Sayı: 1, 12–24. https://ajindex.com/articles/919593
Mohtasham, J. (2015). Review Article-Renewable Energies. Energy Procedia, 74, pp.1289–1297
Li, L., Lin, J., Wu, N., Xie, S., Meng, C., Zheng, Y., Zhao, Y., (2020). Review and Outlook on the International Renewable Energy Development. Energy and Built Environment, 3(2), pp.139-157
Panwar, N. L., Kaushik, S. C., & Kothari, S., (2011). Role of renewable energy sources in environmental protection: A review. Renewable and Sustainable Energy Reviews, 15(3), pp. 1513–1524
Marques Lameirinhas, Ricardo A., João Paulo N. Torres, and João P. de Melo Cunha. (2022). "A photovoltaic technology review: history, fundamentals and applications" Energies, 15(5), pp.1823-1867
Kumar Sahu, B. (2015). A study on global solar PV energy developments and policies with special focus on the top ten solar PV power producing countries. Renewable and Sustainable Energy Reviews, 43, 621-634
Kassem, Y.; Çamur, H.; Alhuoti, S.M.A., (2020). Solar Energy technology for Northern Cyprus: assessment, statistical analysis, and feasibility study. Energies, 13(4), pp. 940-969
Beltran, H., Perez, E., Aparicio, N., & Rodriguez, P. (2013). Daily solar energy estimation for minimizing energy storage requirements in pv power plants. IEEE Transactions on Sustainable Energy, 4(2), pp. 474-481
Sharma, R., & Gidwani, L. (2017). Grid connected solar PV system design and calculation by using PV*SOL premium simulation tool for campus hostels of RTU Kota. 2017 International Conference on Circuit,Power and Computing Technologies (ICCPCT) 20-21 April 2017, Kollam, India. doi:10.1109/iccpct.2017.8074315 [9] Kaczorowska, D., Leonowicz, Z., Rezmer, J., & Janik, P. (2017). Long term performance of a PV system with monocrystalline PV cells — a case study. 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). Milan, Italy. doi:10.1109/eeeic.2017.7977838 [10] Milosavljević, Dragana D., Kevkić, Tijana S. and Jovanović, Slavica J. (2022). Review and validation of photovoltaic solar simulation tools/software based on case study. Open Physics, 20(1), pp. 431-451
Villarrubia, G., De Paz, J. F., Chamoso, P., & la Prieta, F. D. (2018). Artificial neural networks used in optimization problems. Neurocomputing, 272, 10–16
URL-1 Izmir Governorship [Accessed 12 February 2023] [13] URL-2 The World Bank Global Solar Atlas Data [Accessed 12 February 2023] [14] Bakirci, K. (2012