Development of a method to predict building energy consumption through an artificial neural network approach

Ana Paula Melo, Roberto Lamberts, Daniel Cóstola, Jan L M Hensen

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)

Abstract

The main objective of this study is to develop a more accurate method to estimate the energy consumption of commercial buildings at the design stage. The study is based on the simplified model presented in the Regulation for Energy Efficiency Labelling of Commercial Buildings in Brazil. The first step was to evaluate the feasibility and relevance of more complex statistical modelling techniques, such as the neural network. The second step of the assessment consisted of applying the Latin Hypercube sampling technique to combine the effects of several input parameters. Therefore, results of this work may have a profound impact as artificial neural network may be applied in the future in the Brazilian regulation and many other countries. 

Original languageEnglish
Title of host publicationProceedings of BS 2013
Subtitle of host publication13th Conference of the International Building Performance Simulation Association
Pages644-651
Number of pages8
Publication statusPublished - 2013
Event13th Conference of the International Building Performance Simulation Association, BS 2013 - Chambery, France
Duration: 26 Aug 201328 Aug 2013

Conference

Conference13th Conference of the International Building Performance Simulation Association, BS 2013
CountryFrance
CityChambery
Period26/08/1328/08/13

Keywords

  • Regulation for Energy Efficiency Labelling of Commercial Buildings
  • energy consumption
  • commercial buildings
  • neural networks

Fingerprint Dive into the research topics of 'Development of a method to predict building energy consumption through an artificial neural network approach'. Together they form a unique fingerprint.

  • Cite this

    Melo, A. P., Lamberts, R., Cóstola, D., & Hensen, J. L. M. (2013). Development of a method to predict building energy consumption through an artificial neural network approach. In Proceedings of BS 2013 : 13th Conference of the International Building Performance Simulation Association (pp. 644-651)