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Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model

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  • Shimoda, Yoshiyuki
  • Asahi, Takahiro
  • Taniguchi, Ayako
  • Mizuno, Minoru

Abstract

Energy conservation policies for the residential sector are evaluated by a model that simulates city-scale energy consumption in the residential sector by considering the diversity of household and building types. In this model, all the households in the city are classified into 380 categories based on the household and building type. The energy consumption for each household category is simulated by the dynamic energy simulation model, which includes an energy use schedule model and a heating and cooling load calculation model. Since the energy usage of each appliance is simulated for every 5min according to the occupants’ energy usage activity, this model can evaluate not only the energy conservation measures by improving the buildings and appliances but also the measures that involve changing the occupants’ activities. The accuracy of the model is verified by comparing its results with the statistical and the measured data on Osaka City, Japan. Various types of energy conservation measures planned by the Japanese government for the residential sector are simulated and their effects on Osaka City are evaluated quantitatively. The future effects of these combined measures on the energy consumption are also predicted.

Suggested Citation

  • Shimoda, Yoshiyuki & Asahi, Takahiro & Taniguchi, Ayako & Mizuno, Minoru, 2007. "Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model," Energy, Elsevier, vol. 32(9), pages 1617-1633.
    • Handle: RePEc:eee:energy:v:32:y:2007:i:9:p:1617-1633
    DOI: 10.1016/j.energy.2007.01.007
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    1. Brownsword, R.A. & Fleming, P.D. & Powell, J.C. & Pearsall, N., 2005. "Sustainable cities - modelling urban energy supply and demand," Applied Energy, Elsevier, vol. 82(2), pages 167-180, October.
    2. Sezgen, Osman & Koomey, Jonathan G, 2000. "Interactions between lighting and space conditioning energy use in US commercial buildings," Energy, Elsevier, vol. 25(8), pages 793-805.
    3. Michalik, G. & Khan, M.E. & Bonwick, W.J. & Mielczarski, W., 1997. "Structural modelling of energy demand in the residential sector: 1. Development of structural models," Energy, Elsevier, vol. 22(10), pages 937-947.
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