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

There 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...

Full description

Autores:
Liu, Kuang-Sheng
Muda, Iskandar
Lin, Ming-Hung
Dwijendra, Ngakan Ketut Acwin
Caballero, Gaylord Carrillo
Alviz-Meza, Aníbal
Cárdenas-Escrocia, Yulineth
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/12171
Acceso en línea:
https://hdl.handle.net/20.500.12585/12171
Palabra clave:
Electric Power Transmission Networks;
Optimal Power Flow;
Power System
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room
title An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room
spellingShingle An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room
Electric Power Transmission Networks;
Optimal Power Flow;
Power System
title_short An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room
title_full An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room
title_fullStr An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room
title_full_unstemmed An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room
title_sort An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room
dc.creator.fl_str_mv Liu, Kuang-Sheng
Muda, Iskandar
Lin, Ming-Hung
Dwijendra, Ngakan Ketut Acwin
Caballero, Gaylord Carrillo
Alviz-Meza, Aníbal
Cárdenas-Escrocia, Yulineth
dc.contributor.author.none.fl_str_mv Liu, Kuang-Sheng
Muda, Iskandar
Lin, Ming-Hung
Dwijendra, Ngakan Ketut Acwin
Caballero, Gaylord Carrillo
Alviz-Meza, Aníbal
Cárdenas-Escrocia, Yulineth
dc.subject.keywords.spa.fl_str_mv Electric Power Transmission Networks;
Optimal Power Flow;
Power System
topic Electric Power Transmission Networks;
Optimal Power Flow;
Power System
description There 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.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-07-19T21:12:04Z
dc.date.available.none.fl_str_mv 2023-07-19T21:12:04Z
dc.date.issued.none.fl_str_mv 2023
dc.date.submitted.none.fl_str_mv 2023
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dc.identifier.citation.spa.fl_str_mv Liu, 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/su15021728
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/12171
dc.identifier.doi.none.fl_str_mv 10.3390/su15021728
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Liu, 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/su15021728
10.3390/su15021728
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/12171
dc.language.iso.spa.fl_str_mv eng
language eng
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.format.extent.none.fl_str_mv 14 páginas
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dc.publisher.place.spa.fl_str_mv Cartagena de Indias
institution Universidad Tecnológica de Bolívar
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spelling Liu, Kuang-Sheng29aea359-1eea-4ba5-a050-77afca22ac22Muda, Iskandarcc973407-1bc0-4561-9ff5-8bb03e9e42b2Lin, Ming-Hung01ba3a44-067f-4860-a833-2628e5ab6dabDwijendra, Ngakan Ketut Acwinb785574c-f759-487d-82c5-74ab964f209cCaballero, Gaylord Carrillobcbc086f-8893-4b45-af9f-4034118718ccAlviz-Meza, Aníbaledb81688-b57c-4a93-8a69-1d86b83aad87Cárdenas-Escrocia, Yulinetha11f305e-8e1e-4e82-8892-a2161645d9ed2023-07-19T21:12:04Z2023-07-19T21:12:04Z20232023Liu, 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/su15021728https://hdl.handle.net/20.500.12585/1217110.3390/su15021728Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThere 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.14 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Roominfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Electric Power Transmission Networks;Optimal Power Flow;Power SystemCartagena de IndiasPilechiha, P., Mahdavinejad, M., Pour Rahimian, F., Carnemolla, P., Seyedzadeh, S. Multi-objective optimisation framework for designing office windows: quality of view, daylight and energy efficiency (2020) Applied Energy, 261, art. no. 114356. Cited 100 times. https://www.journals.elsevier.com/applied-energy doi: 10.1016/j.apenergy.2019.114356Sadick, A.-M., Kamardeen, I. 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