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...
- Autores:
-
Kuang-Sheng, lu
Iskandar Muda
Ming-Hung, Lin
Ngakan Ketut Acwin Dwijendra
carrillo caraballo, Gaylord
Alviz-Meza, Anibal
cardenas 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:20.500.12585/12423
- Palabra clave:
- gene expression programming
gravitational search algorithm
office room’s window
machine learning
daylight
optimization
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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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 gene expression programming gravitational search algorithm office room’s window machine learning daylight optimization |
| 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 |
Kuang-Sheng, lu Iskandar Muda Ming-Hung, Lin Ngakan Ketut Acwin Dwijendra carrillo caraballo, Gaylord Alviz-Meza, Anibal cardenas escrocia, yulineth |
| dc.contributor.author.none.fl_str_mv |
Kuang-Sheng, lu Iskandar Muda Ming-Hung, Lin Ngakan Ketut Acwin Dwijendra carrillo caraballo, Gaylord Alviz-Meza, Anibal cardenas escrocia, yulineth |
| dc.subject.keywords.spa.fl_str_mv |
gene expression programming gravitational search algorithm office room’s window machine learning daylight optimization |
| topic |
gene expression programming gravitational search algorithm office room’s window machine learning daylight optimization |
| 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. |
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2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-25T12:09:34Z |
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2023-07-25T12:09:34Z |
| dc.date.issued.none.fl_str_mv |
2023-01-16 |
| dc.date.submitted.none.fl_str_mv |
2023-07-24 |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/draft |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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draft |
| dc.identifier.citation.spa.fl_str_mv |
Liu, K.-S.; Muda, I.; Lin, M.-H.; Dwijendra, N.K.A.; Carrillo Caballero, G.; Alviz-Meza, A.;Cárdenas-Escrocia, Y. An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room. Sustainability 2023,15, 1728. https://doi.org/10.3390/su15021728 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12423 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3390/su15021728 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Liu, K.-S.; Muda, I.; Lin, M.-H.; Dwijendra, N.K.A.; Carrillo Caballero, G.; Alviz-Meza, A.;Cárdenas-Escrocia, Y. An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room. Sustainability 2023,15, 1728. https://doi.org/10.3390/su15021728 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12423 https://doi.org/10.3390/su15021728 |
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eng |
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eng |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
| dc.format.extent.none.fl_str_mv |
14 páginas |
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application/pdf |
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Cartagena de Indias |
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Campus Tecnológico |
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Sustainability |
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Universidad Tecnológica de Bolívar |
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Kuang-Sheng, lu67adf9b5-dd17-46bf-be0d-dab9e16d3485Iskandar Muda499909c8-5ba7-45e3-ba3d-fc0483752d41Ming-Hung, Lin2c5578bf-f36b-429c-8286-0c71a06c2125Ngakan Ketut Acwin Dwijendra6d96bbce-effc-4e93-9c8c-44656dfb53c9carrillo caraballo, Gaylord132dcdc6-1619-4c81-8869-eb5853e32fe0Alviz-Meza, Anibal985ee3fd-2926-4b3f-a240-a73fbfd22aebcardenas escrocia, yulinethc34b2ff7-17d2-4340-a0d5-5beb1999ce452023-07-25T12:09:34Z2023-07-25T12:09:34Z2023-01-162023-07-24Liu, K.-S.; Muda, I.; Lin, M.-H.; Dwijendra, N.K.A.; Carrillo Caballero, G.; Alviz-Meza, A.;Cárdenas-Escrocia, Y. An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room. Sustainability 2023,15, 1728. https://doi.org/10.3390/su15021728https://hdl.handle.net/20.500.12585/12423https://doi.org/10.3390/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.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_abf2SustainabilityAn 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_2df8fbb1http://purl.org/coar/version/c_b1a7d7d4d402bccegene expression programminggravitational search algorithmoffice room’s windowmachine learningdaylightoptimizationCartagena de IndiasCampus TecnológicoPúblico generalPilechiha, P.; Mahdavinejad, M.; Pour Rahimian, F.; Carnemolla, P.; Seyedzadeh, S. 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