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dc.contributor.authorGopi, Ajith
dc.contributor.authorSharma, Prabhakar
dc.contributor.authorSudhakar, Kumarasamy
dc.contributor.authorNgui, Wai Keng
dc.contributor.authorKirpichnikova, Irina M.
dc.contributor.authorCüce, Erdem
dc.date.accessioned2023-09-06T10:44:56Z
dc.date.available2023-09-06T10:44:56Z
dc.date.issued2023en_US
dc.identifier.citationGopi, A., Sharma, P., Sudhakar, K., Ngui, W.K., Kirpichnikova, I. & Cüce, E. (2023). Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques. Sustainability, 15(1), 439. https://doi.org/10.3390/su15010439en_US
dc.identifier.issn2071-1050
dc.identifier.urihttps://doi.org/10.3390/su15010439
dc.identifier.urihttps://hdl.handle.net/11436/8275
dc.description.abstractForecasting the performance and energy yield of photovoltaic (PV) farms is crucial for establishing the economic sustainability of a newly installed system. The present study aims to develop a prediction model to forecast an installed PV system's annual power generation yield and performance ratio (PR) using three environmental input parameters: solar irradiance, wind speed, and ambient air temperature. Three data-based artificial intelligence (AI) techniques, namely, adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM), and artificial neural network (ANN), were employed. The models were developed using three years of data from an operational 2MWp Solar PV Project at Kuzhalmannam, Kerala state, India. Statistical indices such as Pearson's R, coefficient of determination (R-2), root-mean-squared error (RMSE), Nash-Sutcliffe efficiency (NSCE), mean absolute-percentage error (MAPE), Kling-Gupta efficiency (KGE), Taylor's diagram, and correlation matrix were used to determine the most accurate prediction model. The results demonstrate that ANFIS was the most precise performance ratio prediction model, with an R-2 value of 0.9830 and an RMSE of 0.6. It is envisaged that the forecast model would be a valuable tool for policymakers, solar energy researchers, and solar farm developers.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectForecastingen_US
dc.subjectSolar irradianceen_US
dc.subjectEnergy generationen_US
dc.subjectSolar planten_US
dc.subjectNeuro-fuzzyen_US
dc.titleWeather impact on solar farm performance: A comparative analysis of machine learning techniquesen_US
dc.typearticleen_US
dc.contributor.departmentRTEÜ, Mühendislik ve Mimarlık Fakültesi, Makine Mühendisliği Bölümüen_US
dc.contributor.institutionauthorCüce, Erdem
dc.identifier.doi10.3390/su15010439en_US
dc.identifier.volume15en_US
dc.identifier.issue1en_US
dc.identifier.startpage439en_US
dc.relation.journalSustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US


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