Integration of logistic regression and multilayer perceptron for intelligent single and dual axis 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.authoridhttps://orcid.org/0000-0002-6143-2734en_US
dc.contributor.authorAl-Rousan, Nadia
dc.contributor.authorIsa, Nor Ashidi Mat
dc.contributor.authorDesa, Mohammad Khairunaz Mat
dc.contributor.authorAl-Najjar, Hazem
dc.date.accessioned2023-07-29T14:09:46Z
dc.date.available2023-07-29T14:09:46Z
dc.date.issued2021en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractIntelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. Several low performance intelligent solar tracking systems have been designed and implemented. Multilayer perceptron (MLP) is one of the common controllers that used to drive solar tracking systems. However, when the input data are complex for neural network, neural network would not well explain the relationship between these data. Thus, it performed worse than when the input data are simple. Using a premapping of relationship between samples of data as input to neural network along with the original input data could probably a strong guide to help neural network to reach the desired goal and predict the output variables faster and more accurate. It is found that using the output of logistic regression as input to neural network would faster the process of finding the predicted output by neural network. Thus, this study aims to propose new efficient and low complexity single and dual axis solar tracking systems by integrating supervised logistic regression (LR) and supervised MLP or cascade multilayer perceptron (CMLP). LR models are trained by using one of unsupervised clustering techniques (k?means, fuzzy c?means, and hierarchical clustering algorithms). The proposed models were used to predict both tilt and orientation angles by two different data sets (month, day, and time variables data set) and (month, day, time, Isc, Voc, and power radiation variables data sets). The results revealed that the proposed MLP/CMLP?LR systems are able to increase the prediction rate and decrease the mean square error rate as compared to conventional models in both single and dual axis solar tracking systems. The new developed intelligent systems achieved less number of overall connections, less number of neurons, and less time complexity. The finding suggests that the proposed intelligent solar tracking systems has a great potential to be applied for real?world applications (i.e., solar heating systems, solar lightening systems, factories, and solar powered ventilation.en_US
dc.identifier.doi10.1002/int.22525en_US
dc.identifier.endpage5669en_US
dc.identifier.issn0884-8173
dc.identifier.issn1098-111X
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85108017826en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage5605en_US
dc.identifier.urihttps://hdl.handle.net/11363/5117
dc.identifier.urihttps://doi.org/
dc.identifier.volume36en_US
dc.identifier.wosWOS:000661909200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAl-Rousan, Nadia
dc.institutionauthorAl-Najjar, Hazem
dc.language.isoenen_US
dc.publisherWILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJen_US
dc.relation.ispartofInternational Journal of Intelligent Systemsen_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.subjectcomplexityen_US
dc.subjectintelligent systemsen_US
dc.subjectlogistic regressionen_US
dc.subjectmultilayer perceptronen_US
dc.subjectneural networken_US
dc.subjectsingle and dual axisen_US
dc.titleIntegration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systemsen_US
dc.typeArticleen_US

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