dc.contributor.author | Al-Rousan, Nadia | |
dc.contributor.author | Isa, Nor Ashidi Mat | |
dc.contributor.author | Desa, Mohammad Khairunaz Mat | |
dc.contributor.author | Al-Najjar, Hazem | |
dc.date.accessioned | 2023-07-29T14:09:46Z | |
dc.date.available | 2023-07-29T14:09:46Z | |
dc.date.issued | 2021 | en_US |
dc.identifier.issn | 0884-8173 | |
dc.identifier.issn | 1098-111X | |
dc.identifier.uri | https://hdl.handle.net/11363/5117 | |
dc.description.abstract | Intelligent 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.language.iso | eng | en_US |
dc.publisher | WILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ | en_US |
dc.relation.isversionof | 10.1002/int.22525 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | complexity | en_US |
dc.subject | intelligent systems | en_US |
dc.subject | logistic regression | en_US |
dc.subject | multilayer perceptron | en_US |
dc.subject | neural network | en_US |
dc.subject | single and dual axis | en_US |
dc.title | Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems | en_US |
dc.type | article | en_US |
dc.relation.ispartof | International Journal of Intelligent Systems | en_US |
dc.department | Mühendislik ve Mimarlık Fakültesi | en_US |
dc.authorid | https://orcid.org/0000-0001-8451-898X | en_US |
dc.authorid | https://orcid.org/0000-0002-2675-4914 | en_US |
dc.authorid | https://orcid.org/0000-0002-3903-1133 | en_US |
dc.authorid | https://orcid.org/0000-0002-6143-2734 | en_US |
dc.identifier.volume | 36 | en_US |
dc.identifier.issue | 10 | en_US |
dc.identifier.startpage | 5605 | en_US |
dc.identifier.endpage | 5669 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.institutionauthor | Al-Rousan, Nadia | |
dc.institutionauthor | Al-Najjar, Hazem | |