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An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room

dc.contributor.authorLiu, Kuang-Sheng
dc.contributor.authorMuda, Iskandar
dc.contributor.authorLin, Ming-Hung
dc.contributor.authorDwijendra, Ngakan Ketut Acwin
dc.contributor.authorCaballero, Gaylord Carrillo
dc.contributor.authorAlviz-Meza, Aníbal
dc.contributor.authorCárdenas-Escrocia, Yulineth
dc.date.accessioned2023-07-19T21:12:04Z
dc.date.available2023-07-19T21:12:04Z
dc.date.issued2023
dc.date.submitted2023
dc.identifier.citationLiu, Kuang-Sheng, Iskandar Muda, Ming-Hung Lin, Ngakan Ketut Acwin Dwijendra, Gaylord Carrillo Caballero, Aníbal Alviz-Meza, and Yulineth Cárdenas-Escrocia. 2023. "An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room" Sustainability 15, no. 2: 1728. https://doi.org/10.3390/su15021728spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/12171
dc.description.abstractThere are no exact criteria for the architecture of openings and windows in office buildings in order to optimize energy consumption. Due to the physical limitations of this renewable energy source and the lack of conscious control over its capabilities, the amount of light entering offices and the role of daylight as a source of energy are determined by how they are constructed. In this study, the standard room dimensions, which are suitable for three to five employees, are compared to computer simulations. DesignBuilder and EnergyPlus are utilized to simulate the office’s lighting and energy consumption. This study presents a new method for estimating conventional energy consumption based on gene expression programming (GEP). A gravitational search algorithm (GSA) is implemented in order to optimize the model results. Using input and output data collected from a simulation of conventional energy use, the physical law underlying the problem and the relationship between inputs and outputs are identified. This method has the advantages of being quick and accurate, with no simulation required. Based on effective input parameters and sensitivity analysis, four models are evaluated. These models are used to evaluate the performance of the trained network based on statistical indicators. Among all the GEP models tested in this study, the one with the lowest MAE (0.1812) and RMSE (0.09146) and the highest correlation coefficient (0.90825) is found to be the most accurate. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.spa
dc.format.extent14 páginas
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleAn Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Roomspa
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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_b1a7d7d4d402bccespa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasversioninfo:eu-repo/semantics/draftspa
dc.identifier.doi10.3390/su15021728
dc.subject.keywordsElectric Power Transmission Networks;spa
dc.subject.keywordsOptimal Power Flow;spa
dc.subject.keywordsPower Systemspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.type.spahttp://purl.org/coar/resource_type/c_6501spa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_6501spa


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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.