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
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Objective & Hypothesis:Global solar energy has become a popular investment choice for investors, with installed power reaching 940GW according to 2021 data.
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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.
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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, Volume: 3 Issue: 1. https://ajindex.com/articles/1609619
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