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Understanding energy consumption in high-performance social housing buildings: A case study from Canada

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  • Rouleau, Jean
  • Gosselin, Louis
  • Blanchet, Pierre

Abstract

This paper presents a case study of a recently built high-performance Canadian social housing building with the aim of comparing the expected and measured energy consumptions and to identify the parameters affecting the most the energy need. A monitoring system compiles at a 10-min frequency information related to the energy use and the thermal conditions observed in the building and its HVAC system. The building has the particularity of comprising two symmetric sections made of different timber structure systems. No significant differences of energy consumption were detected between the two parts of the buildings. However, a large variance was observed when comparing each dwelling individually regardless of their structures. The orientation of the dwelling also exhibited a minimal influence compared to these variations, suggesting that occupant behavior is the dominant factor explaining dwelling-to-dwelling variability and is thus critical for understanding energy use in residential buildings. Regression analysis showed that specific occupant actions, such as opening windows in winter or using electrical appliances, have a great impact on the energy balance of the apartments. In 2016, the performance gap between measured and expected total energy demand of the building was 74%. With the use of the large dataset coming from the building, it was possible to determine the causes behind this large gap for the reference building.

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  • Rouleau, Jean & Gosselin, Louis & Blanchet, Pierre, 2018. "Understanding energy consumption in high-performance social housing buildings: A case study from Canada," Energy, Elsevier, vol. 145(C), pages 677-690.
    • Handle: RePEc:eee:energy:v:145:y:2018:i:c:p:677-690
    DOI: 10.1016/j.energy.2017.12.107
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    1. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    2. Emery, A.F. & Kippenhan, C.J., 2006. "A long term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards," Energy, Elsevier, vol. 31(5), pages 677-693.
    3. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    4. Langevin, Jared & Gurian, Patrick L. & Wen, Jin, 2013. "Reducing energy consumption in low income public housing: Interviewing residents about energy behaviors," Applied Energy, Elsevier, vol. 102(C), pages 1358-1370.
    5. Fan, Rui & Jiang, Yiqiang & Yao, Yang & Shiming, Deng & Ma, Zuiliang, 2007. "A study on the performance of a geothermal heat exchanger under coupled heat conduction and groundwater advection," Energy, Elsevier, vol. 32(11), pages 2199-2209.
    6. Kneifel, Joshua & Webb, David, 2016. "Predicting energy performance of a net-zero energy building: A statistical approach," Applied Energy, Elsevier, vol. 178(C), pages 468-483.
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    5. Charles Breton & Pierre Blanchet & Ben Amor & Robert Beauregard & Wen-Shao Chang, 2018. "Assessing the Climate Change Impacts of Biogenic Carbon in Buildings: A Critical Review of Two Main Dynamic Approaches," Sustainability, MDPI, vol. 10(6), pages 1-30, June.
    6. Xia Wang & Jiachen Yuan & Kairui You & Xianrui Ma & Zhaoji Li, 2023. "Using Real Building Energy Use Data to Explain the Energy Performance Gap of Energy-Efficient Residential Buildings: A Case Study from the Hot Summer and Cold Winter Zone in China," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    7. Rouleau, Jean & Gosselin, Louis, 2021. "Impacts of the COVID-19 lockdown on energy consumption in a Canadian social housing building," Applied Energy, Elsevier, vol. 287(C).
    8. Ozarisoy, B. & Altan, H., 2022. "Significance of occupancy patterns and habitual household adaptive behaviour on home-energy performance of post-war social-housing estate in the South-eastern Mediterranean climate: Energy policy desi," Energy, Elsevier, vol. 244(PB).
    9. Ardeshir Mahdavi & Christiane Berger & Hadeer Amin & Eleni Ampatzi & Rune Korsholm Andersen & Elie Azar & Verena M. Barthelmes & Matteo Favero & Jakob Hahn & Dolaana Khovalyg & Henrik N. Knudsen & Ale, 2021. "The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?," Sustainability, MDPI, vol. 13(6), pages 1-44, March.
    10. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    11. Maltais, Louis-Gabriel & Gosselin, Louis, 2021. "Predictability analysis of domestic hot water consumption with neural networks: From single units to large residential buildings," Energy, Elsevier, vol. 229(C).
    12. Rouleau, Jean & Gosselin, Louis & Blanchet, Pierre, 2019. "Robustness of energy consumption and comfort in high-performance residential building with respect to occupant behavior," Energy, Elsevier, vol. 188(C).
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