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Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

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  • Behrad Bezyan

    (Centre for Zero Energy Buildings Studies, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec H3G 1M8, Canada)

  • Radu Zmeureanu

    (Centre for Zero Energy Buildings Studies, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec H3G 1M8, Canada)

Abstract

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

Suggested Citation

  • Behrad Bezyan & Radu Zmeureanu, 2020. "Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada," Energies, MDPI, vol. 13(5), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1158-:d:328155
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    References listed on IDEAS

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    2. Monfet, Danielle & Zmeureanu, Radu, 2012. "Ongoing commissioning of water-cooled electric chillers using benchmarking models," Applied Energy, Elsevier, vol. 92(C), pages 99-108.
    3. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    4. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
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    Cited by:

    1. Junjun Shi & Jingfang Shen & Yaohui Li, 2021. "High-Precision Kriging Modeling Method Based on Hybrid Sampling Criteria," Mathematics, MDPI, vol. 9(5), pages 1-25, March.
    2. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

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