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A model of residential energy end-use in Canada: Using conditional demand analysis to suggest policy options for community energy planners

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  • Newsham, Guy R.
  • Donnelly, Cara L.

Abstract

We applied conditional demand analysis (CDA) to estimate the average annual energy use of various electrical and natural gas appliances, and derived energy reductions associated with certain appliance upgrades and behaviours. The raw data came from 9773 Canadian households, and comprised annual electricity and natural gas use, and responses to >600 questions on dwelling and occupant characteristics, appliances, heating and cooling equipment, and associated behaviours. Replacing an old (>10 years) refrigerator with a new one was estimated to save 100kWh/year; replacing an incandescent lamp with a CFL/LED lamp was estimated to save 20kWh/year; and upgrading an old central heating system with a new one was estimated to save 2000kWh/year. This latter effect was similar to that of reducing the number of walls exposed to the outside. Reducing the winter thermostat setpoint during occupied, waking hours was estimated to lower annual energy use by 200kWh/°C-reduction, and lowering the thermostat setting overnight in winter relative to the setting during waking hours (night-time setback) was estimated to have a similar effect. This information may be used by policy-makers to optimize incentive programs, information campaigns, or other energy use change instruments.

Suggested Citation

  • Newsham, Guy R. & Donnelly, Cara L., 2013. "A model of residential energy end-use in Canada: Using conditional demand analysis to suggest policy options for community energy planners," Energy Policy, Elsevier, vol. 59(C), pages 133-142.
    • Handle: RePEc:eee:enepol:v:59:y:2013:i:c:p:133-142
    DOI: 10.1016/j.enpol.2013.02.030
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    References listed on IDEAS

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    1. Newsham, Guy R. & Birt, Benjamin J. & Rowlands, Ian H., 2011. "A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use," Energy Policy, Elsevier, vol. 39(10), pages 6376-6389, October.
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    Cited by:

    1. Andrea Menapace & Simone Santopietro & Rudy Gargano & Maurizio Righetti, 2021. "Stochastic Generation of District Heat Load," Energies, MDPI, vol. 14(17), pages 1-17, August.
    2. Karunathilake, Hirushie & Hewage, Kasun & Sadiq, Rehan, 2018. "Opportunities and challenges in energy demand reduction for Canadian residential sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2005-2016.
    3. Shigeru Matsumoto, 2015. "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Proceedings of International Academic Conferences 3105452, International Institute of Social and Economic Sciences.
    4. Onuma, Hiroki & Matsumoto, Shigeru & Arimura, Toshi H., 2020. "How much household electricity consumption is actually saved by replacement with Light-Emitting Diodes (LEDs)?," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 224-238.
    5. Soo-Jin Lee & You-Jeong Kim & Hye-Sun Jin & Sung-Im Kim & Soo-Yeon Ha & Seung-Yeong Song, 2019. "Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis," Energies, MDPI, vol. 12(12), pages 1-18, June.
    6. Ian H. Rowlands & Tobi Reid & Paul Parker, 2015. "Research with disaggregated electricity end‐use data in households: review and recommendations," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 4(5), pages 383-396, September.
    7. Inoue, Nozomu & Matsumoto, Shigeru, 2019. "An examination of losses in energy savings after the Japanese Top Runner Program?," Energy Policy, Elsevier, vol. 124(C), pages 312-319.
    8. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.
    9. 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.
    10. Hannah Villeneuve & Ahmed Abdeen & Maya Papineau & Sharane Simon & Cynthia Cruickshank & William O'Brien, 2020. "New insights on the energy impacts of telework," Carleton Economic Papers 20-20, Carleton University, Department of Economics.
    11. Jia, Jun-Jun & Ni, Jinlan & Wei, Chu, 2023. "Residential responses to service-specific electricity demand: Case of China," China Economic Review, Elsevier, vol. 78(C).
    12. Papineau, Maya & Yassin, Kareman & Newsham, Guy & Brice, Sarah, 2021. "Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    13. Shiraki, Hiroto & Nakamura, Shogo & Ashina, Shuichi & Honjo, Keita, 2016. "Estimating the hourly electricity profile of Japanese households – Coupling of engineering and statistical methods," Energy, Elsevier, vol. 114(C), pages 478-491.
    14. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    15. Cheng, Shulei & Wang, Kexin & Meng, Fanxin & Liu, Gengyuan & An, Jiafu, 2024. "The unanticipated role of fiscal environmental expenditure in accelerating household carbon emissions: Evidence from China," Energy Policy, Elsevier, vol. 185(C).
    16. Matsumoto, Shigeru, 2016. "How do household characteristics affect appliance usage? Application of conditional demand analysis to Japanese household data," Energy Policy, Elsevier, vol. 94(C), pages 214-223.

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