Correlation analysis and MLP/CMLP for optimum variables to predict orientation and tilt angles in intelligent solar tracking systems

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dc.authoridhttps://orcid.org/0000-0001-8451-898Xen_US
dc.authoridhttps://orcid.org/0000-0002-2675-4914en_US
dc.authoridhttps://orcid.org/0000-0002-3903-1133en_US
dc.contributor.authorAl-Rousan, Nadia
dc.contributor.authorIsa, Nor Ashidi Mat
dc.contributor.authorDesa, Mohd Khairunaz Mat
dc.date.accessioned2023-09-20T05:58:38Z
dc.date.available2023-09-20T05:58:38Z
dc.date.issued2021en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractDifferent solar tracking variables have been employed to build intelligent solar tracking systems without considering the dominant and optimum ones. Thus, several low performance intelligent solar tracking systems have been designed and implemented due to the inappropriate combination of solar tracking variables and intelligent predictors to drive the solar trackers. This research aims to investigate and evaluate the most effective and dominant variables on dualand single-axis solar trackers and to find the appropriate combination of solar variables and intelligent predictors. The optimum variables will be found by using correlation results between different variables and both orientation and tilt angles. Then, to use the selected variables to develop different intelligent solar trackers. The results revealed that month, day, and time are the most effective variables for horizontal single-axis and dual-axis solar tracking systems. Using these variables in cascade multilayer perceptron (CMLP) and multilayer perceptron (MLP) produced high performance. These predictors could predict both orientation and tilt angles efficiently. It is found that day variable is very effective to increase the performance of solar trackers although day variable is neither correlated nor significant with both orientation and tilt angles. Linear regression predicted less than 70% of the given data in most cases, whereas nonlinear models could predict the optimum orientation and tilt angles. In single-axis tracker, month, day, and time variables achieved prediction rates of 96.85% and 96.83% for three hidden layers of MLP and CMLP, respectively, whereas the MSE are 0.0025 and 0.0008, respectively. In dual-axis solar tracker, MLP and CMLP predicted 96.68% and 97.98% respectively, with MSE of 0.0007 for both.en_US
dc.identifier.doi10.1002/er.5676en_US
dc.identifier.endpage477en_US
dc.identifier.issn0363-907X
dc.identifier.issn1099-114X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85088862400en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage453en_US
dc.identifier.urihttps://hdl.handle.net/11363/5561
dc.identifier.urihttps://doi.org/
dc.identifier.volume45en_US
dc.identifier.wosWOS:000554079600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAl-Rousan, Nadia
dc.language.isoenen_US
dc.publisherWILEY-HINDAWI, ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON WIT 5HE, ENGLANDen_US
dc.relation.ispartofInternational Journal of Energy Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectlinear regressionen_US
dc.subjectcascade multilayer perceptronen_US
dc.subjectdual-axisen_US
dc.subjecthorizontal single-axisen_US
dc.subjectintelligent solar tracking systemsen_US
dc.subjectmultilayer perceptron neural networksen_US
dc.subjectphotovoltaicen_US
dc.titleCorrelation analysis and MLP/CMLP for optimum variables to predict orientation and tilt angles in intelligent solar tracking systemsen_US
dc.typeArticleen_US

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