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Effects of socioeconomic factors on household appliance, lighting, and space cooling electricity consumption

Author

Listed:
  • Merih Aydinalp
  • V. Ismet Ugursal
  • Alan S. Fung

Abstract

Two methods are currently used to model residential energy consumption at the national or regional level: the engineering method and the conditional demand analysis (CDA) method. One of the major difficulties associated with the use of engineering models is the inclusion of consumer behaviour and socioeconomic factors that have significant effects on the residential energy consumption. The CDA method can handle socioeconomic factors if they are included in the model formulation. However, the multicollinearity problem and the need for a very large amount of data make the use of CDA models very difficult. It is shown in this paper that the neural network (NN) method can be used to model the residential energy consumption with the inclusion of socioeconomic factors. The appliances, lighting, and cooling component of the NN based energy consumption model developed for the Canadian residential sector is presented here and the effects of some socioeconomic factors on the residential energy consumption are examined using the model.

Suggested Citation

  • Merih Aydinalp & V. Ismet Ugursal & Alan S. Fung, 2003. "Effects of socioeconomic factors on household appliance, lighting, and space cooling electricity consumption," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 20(3), pages 302-315.
    • Handle: RePEc:ids:ijgeni:v:20:y:2003:i:3:p:302-315
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    Citations

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    Cited by:

    1. 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.
    2. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    3. Wang, Guimei & Mukhtar, Azfarizal & Moayedi, Hossein & Khalilpoor, Nima & Tt, Quynh, 2024. "Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector," Energy, Elsevier, vol. 298(C).
    4. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    5. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    6. 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.
    7. Wallis, Hannah & Nachreiner, Malte & Matthies, Ellen, 2016. "Adolescents and electricity consumption; Investigating sociodemographic, economic, and behavioural influences on electricity consumption in households," Energy Policy, Elsevier, vol. 94(C), pages 224-234.
    8. Wenliang Li, 2020. "Quantifying the Building Energy Dynamics of Manhattan, New York City, Using an Urban Building Energy Model and Localized Weather Data," Energies, MDPI, vol. 13(12), pages 1-22, June.

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