Comparison of machine learning techniques for predicting energy loads in buildings
Machine learning methods can be used to help design energy-efficient buildings reducing energy loads while maintaining the desired internal temperature. They work by estimating a response from a set of inputs such as building geometry, material properties, project costs, local weather conditions, as...
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| मुख्य लेखकों: | , , , |
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| स्वरूप: | Online |
| भाषा: | eng |
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ANTAC - Associação Nacional de Tecnologia do Ambiente Construído
2017
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| ऑनलाइन पहुंच: | https://seer.ufrgs.br/ambienteconstruido/article/view/69635 |
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ojs-article-696352017-06-30T15:35:30Z Comparison of machine learning techniques for predicting energy loads in buildings Duarte, Grasiele Regina Fonseca, Leonardo Goliatt da Goliatt, Priscila Vanessa Zabala Capriles Lemonge, Afonso Celso de Castro Energy Efficiency; Heating and Cooling Loads; Machine Learning Machine learning methods can be used to help design energy-efficient buildings reducing energy loads while maintaining the desired internal temperature. They work by estimating a response from a set of inputs such as building geometry, material properties, project costs, local weather conditions, as well as environmental impacts. These methods require a training phase which considers a dataset drawn from selected variables in the problem domain. This paper evaluates the performance of four machine learning methods to predict cooling and heating loads of residential buildings. The dataset consists of 768 samples with eight input variables and two output variables derived from building designs. The methods were selected based on exhaustive research with cross validation. Four statistical measures and one synthesis index were used for the performance assessment and comparison. The proposed framework resulted in accurate prediction models with optimized parameters that can potentially avoid modeling and testing various designs, helping to economize in the initial phase of the project. ANTAC - Associação Nacional de Tecnologia do Ambiente Construído FAPEMIG CAPES CNPq 2017-06-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://seer.ufrgs.br/ambienteconstruido/article/view/69635 Ambiente Construído; v. 17, n. 3 (2017); 103-115 Ambiente Construído; v. 17, n. 3 (2017); 103-115 Ambiente Construído; v. 17, n. 3 (2017); 103-115 1678-8621 1415-8876 eng https://seer.ufrgs.br/ambienteconstruido/article/view/69635/42241 Direitos autorais 2017 Ambiente Construído https://creativecommons.org/licenses/by/4.0 |
| institution |
Universidade Federal do Rio Grande do Sul |
| collection |
OJS |
| language |
eng |
| format |
Online |
| author |
Duarte, Grasiele Regina Fonseca, Leonardo Goliatt da Goliatt, Priscila Vanessa Zabala Capriles Lemonge, Afonso Celso de Castro |
| spellingShingle |
Duarte, Grasiele Regina Fonseca, Leonardo Goliatt da Goliatt, Priscila Vanessa Zabala Capriles Lemonge, Afonso Celso de Castro Comparison of machine learning techniques for predicting energy loads in buildings |
| author_facet |
Duarte, Grasiele Regina Fonseca, Leonardo Goliatt da Goliatt, Priscila Vanessa Zabala Capriles Lemonge, Afonso Celso de Castro |
| author_sort |
Duarte, Grasiele Regina |
| title |
Comparison of machine learning techniques for predicting energy loads in buildings |
| title_short |
Comparison of machine learning techniques for predicting energy loads in buildings |
| title_full |
Comparison of machine learning techniques for predicting energy loads in buildings |
| title_fullStr |
Comparison of machine learning techniques for predicting energy loads in buildings |
| title_full_unstemmed |
Comparison of machine learning techniques for predicting energy loads in buildings |
| title_sort |
comparison of machine learning techniques for predicting energy loads in buildings |
| description |
Machine learning methods can be used to help design energy-efficient buildings reducing energy loads while maintaining the desired internal temperature. They work by estimating a response from a set of inputs such as building geometry, material properties, project costs, local weather conditions, as well as environmental impacts. These methods require a training phase which considers a dataset drawn from selected variables in the problem domain. This paper evaluates the performance of four machine learning methods to predict cooling and heating loads of residential buildings. The dataset consists of 768 samples with eight input variables and two output variables derived from building designs. The methods were selected based on exhaustive research with cross validation. Four statistical measures and one synthesis index were used for the performance assessment and comparison. The proposed framework resulted in accurate prediction models with optimized parameters that can potentially avoid modeling and testing various designs, helping to economize in the initial phase of the project. |
| publisher |
ANTAC - Associação Nacional de Tecnologia do Ambiente Construído |
| publishDate |
2017 |
| url |
https://seer.ufrgs.br/ambienteconstruido/article/view/69635 |
| work_keys_str_mv |
AT duartegrasieleregina comparisonofmachinelearningtechniquesforpredictingenergyloadsinbuildings AT fonsecaleonardogoliattda comparisonofmachinelearningtechniquesforpredictingenergyloadsinbuildings AT goliattpriscilavanessazabalacapriles comparisonofmachinelearningtechniquesforpredictingenergyloadsinbuildings AT lemongeafonsocelsodecastro comparisonofmachinelearningtechniquesforpredictingenergyloadsinbuildings |
| _version_ |
1709370657368702976 |