Uz, ÖzgünÖzmen, Özge Tüzün2025-03-212025-03-2120232757-9778https://hdl.handle.net/20.500.14034/2761https://dergipark.org.tr/tr/pub/aita/issue/77113/1251300Global 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.eninfo:eu-repo/semantics/openAccessphotovoltaic energyenergy estimation methodsartificial neural networksArticle3124-Dec