Estimating building energy consumption using extreme learning machine method

Sareh Naji, Afram Keivani, Shahaboddin Shamshirband, U. Johnson Alengaram, Mohd Zamin Jumaat, Zulkefli Mansor, Malrey Lee

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN.

Original languageEnglish
Pages (from-to)506-516
Number of pages11
JournalEnergy
Volume97
DOIs
Publication statusPublished - 15 Feb 2016

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Learning systems
Energy utilization
Genetic programming
Neural networks
Thermal insulation
Application programs
Energy efficiency
Insulation
Earth (planet)
Engineers

Keywords

  • ELM (extreme learning machine)
  • Energy consumption
  • Energy efficiency
  • Estimation
  • Residential buildings

ASJC Scopus subject areas

  • Energy(all)
  • Pollution

Cite this

Naji, S., Keivani, A., Shamshirband, S., Alengaram, U. J., Jumaat, M. Z., Mansor, Z., & Lee, M. (2016). Estimating building energy consumption using extreme learning machine method. Energy, 97, 506-516. https://doi.org/10.1016/j.energy.2015.11.037

Estimating building energy consumption using extreme learning machine method. / Naji, Sareh; Keivani, Afram; Shamshirband, Shahaboddin; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Mansor, Zulkefli; Lee, Malrey.

In: Energy, Vol. 97, 15.02.2016, p. 506-516.

Research output: Contribution to journalArticle

Naji, S, Keivani, A, Shamshirband, S, Alengaram, UJ, Jumaat, MZ, Mansor, Z & Lee, M 2016, 'Estimating building energy consumption using extreme learning machine method', Energy, vol. 97, pp. 506-516. https://doi.org/10.1016/j.energy.2015.11.037
Naji S, Keivani A, Shamshirband S, Alengaram UJ, Jumaat MZ, Mansor Z et al. Estimating building energy consumption using extreme learning machine method. Energy. 2016 Feb 15;97:506-516. https://doi.org/10.1016/j.energy.2015.11.037
Naji, Sareh ; Keivani, Afram ; Shamshirband, Shahaboddin ; Alengaram, U. Johnson ; Jumaat, Mohd Zamin ; Mansor, Zulkefli ; Lee, Malrey. / Estimating building energy consumption using extreme learning machine method. In: Energy. 2016 ; Vol. 97. pp. 506-516.
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