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A model of residential energy use in New Zealand

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  • Saha, G.P.
  • Stephenson, J.

Abstract

This paper describes an engineering-economic model which was developed to simulate residential energy use in New Zealand from 1976 to 2000. The model is sensitive to major technological, economic and demographic determinants. It deals with energy use at the national level for four types of fuels (electricity, oil, gas, and “other”) and four types of end uses (space heating, water heating, cooking, and “other”). Simulations from 1961 to 1978 show that the model accurately predicts data on energy use, appliance stock, intensity of use of the appliances, and the construction of new housing units for the historical simulation and ex-post forecast periods. The model demonstrates that the potential of conservation programmes employing proven technology to reduce energy use can be substantial; in fact, it is possible to achieve a nearly zero energy growth with little effect on life style.

Suggested Citation

  • Saha, G.P. & Stephenson, J., 1980. "A model of residential energy use in New Zealand," Energy, Elsevier, vol. 5(2), pages 167-175.
  • Handle: RePEc:eee:energy:v:5:y:1980:i:2:p:167-175
    DOI: 10.1016/0360-5442(80)90005-5
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    Cited by:

    1. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    2. Wei Yu & Baizhan Li & Yarong Lei & Meng Liu, 2011. "Analysis of a Residential Building Energy Consumption Demand Model," Energies, MDPI, vol. 4(3), pages 1-13, March.
    3. Marcin Zygmunt & Dariusz Gawin, 2021. "Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock," Energies, MDPI, vol. 14(24), pages 1-15, December.
    4. Guarino, Francesco & Cassarà, Pietro & Longo, Sonia & Cellura, Maurizio & Ferro, Erina, 2015. "Load match optimisation of a residential building case study: A cross-entropy based electricity storage sizing algorithm," Applied Energy, Elsevier, vol. 154(C), pages 380-391.
    5. Wang, Manyu & Wei, Chu, 2024. "Toward sustainable heating: Assessment of the carbon mitigation potential from residential heating in northern rural China," Energy Policy, Elsevier, vol. 190(C).
    6. 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.
    7. 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).
    8. Garshasbi, Samira & Kurnitski, Jarek & Mohammadi, Yousef, 2016. "A hybrid Genetic Algorithm and Monte Carlo simulation approach to predict hourly energy consumption and generation by a cluster of Net Zero Energy Buildings," Applied Energy, Elsevier, vol. 179(C), pages 626-637.

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