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Estimating building energy consumption using extreme learning machine method

Author

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

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.

Suggested Citation

  • Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
    • Handle: RePEc:eee:energy:v:97:y:2016:i:c:p:506-516
    DOI: 10.1016/j.energy.2015.11.037
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    References listed on IDEAS

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    1. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    2. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    3. Kaygusuz, Kamil & Kaygusuz, Abdullah, 2002. "Renewable energy and sustainable development in Turkey," Renewable Energy, Elsevier, vol. 25(3), pages 431-453.
    4. Naji, Sareh & Shamshirband, Shahaboddin & Basser, Hossein & Keivani, Afram & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Petković, Dalibor, 2016. "Application of adaptive neuro-fuzzy methodology for estimating building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1520-1528.
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