Comparison of machine learning techniques for predicting energy loads in buildings

Autores

  • Grasiele Regina Duarte Universidade Federal de Juiz de Fora
  • Leonardo Goliatt da Fonseca Universidade Federal de Juiz de Fora http://orcid.org/0000-0002-2844-9470
  • Priscila Vanessa Zabala Capriles Goliatt Universidade Federal de Juiz de Fora
  • Afonso Celso de Castro Lemonge Universidade Federal de Juiz de Fora

Palavras-chave:

Energy Efficiency, Heating and Cooling Loads, Machine Learning

Resumo

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.

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Biografia do Autor

Grasiele Regina Duarte, Universidade Federal de Juiz de Fora

Graduada em Ciência da Computação e mestra em Modelagem Computacional. Atualmente cursa doutorado no Programa de Pós-graduação em Modelagem Computacional pela Universidade Federal de Juiz de Fora.

Publicado

30.06.2017

Como Citar

DUARTE, G. R.; FONSECA, L. G. da; GOLIATT, P. V. Z. C.; LEMONGE, A. C. de C. Comparison of machine learning techniques for predicting energy loads in buildings. Ambiente Construído, [S. l.], v. 17, n. 3, p. 103–115, 2017. Disponível em: https://seer.ufrgs.br/index.php/ambienteconstruido/article/view/69635. Acesso em: 28 mar. 2024.

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