Machine learning applications for photovoltaic system optimization in zero green energy buildings

dc.creatorLiu, Wei
dc.creatorShen, Yedan
dc.creatorAungkulanon, Pasura
dc.creatorGhalandari, Mohammad
dc.creatorLe, Binh Nguyen
dc.creatorAlviz Meza, Anibal
dc.creatorCardenas Escorcia, Yulineth
dc.date2023-08-10T21:58:47Z
dc.date2023-08-10T21:58:47Z
dc.date2023
dc.date.accessioned2023-10-03T20:00:31Z
dc.date.available2023-10-03T20:00:31Z
dc.identifierWei Liu, Yedan Shen, Pasura Aungkulanon, Mohammad Ghalandari, Binh Nguyen Le, Aníbal Alviz-Meza, Yulineth Cárdenas-Escrocia, Machine learning applications for photovoltaic system optimization in zero green energy buildings, Energy Reports, Volume 9, 2023, Pages 2787-2796, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2023.01.114
dc.identifierhttps://hdl.handle.net/11323/10379
dc.identifier10.1016/j.egyr.2023.01.114
dc.identifier2352-4847
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9173812
dc.descriptionIn this paper, the energy supply of a zero-energy building with 220 square meters is considered using optimized nanocomposite solar panels with respect to maximum efficiency. An optimized hybrid machine learning method plays a key role in presenting solar panel modeling with over 0.99% accuracy. Predicting the properties of the nanomaterial solar cell in four different seasons is performed by efficient support vector machines (SVM), and k-nearest neighbors (KNN) machine learning algorithms. In addition, the KNN algorithm is optimized by the Particle Swarm Optimization (PSO) method to improve the capabilities of KNN and reveal the best performance criteria for the photovoltaic modeling characteristics. The parameters of the nanocomposite cells are optimized using the proposed novel Multidisciplinary Optimization Method (MDO) to increase the efficiency of the solar panel by up to 170%. Optimization of solar cell performance with nanocomposite material under energy consumption constraints is carried out to propose the best construction of cells with 3 layers. The presented approach as a solution and indicator for the next generation of commercial and residential buildings can increase the potential values of solar cells to at least 70%.
dc.format10 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier Ltd.
dc.publisherUnited Kingdom
dc.relationEnergy Reports
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dc.rights© 2023 The Authors. Published by Elsevier Ltd.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S2352484723001221
dc.subjectZero energy buildings
dc.subjectMachine learning
dc.subjectOptimization
dc.subjectPhotovoltaic systems
dc.subjectSolar panel
dc.subjectNano-composite material
dc.titleMachine learning applications for photovoltaic system optimization in zero green energy buildings
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_970fb48d4fbd8a85


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