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Structural modelling of energy demand in the residential sector: 2. The use of linguistic variables to include uncertainty of customers' behaviour

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  • Michalik, G.
  • Khan, M.E.
  • Bonwick, W.J.
  • Mielczarski, W.

Abstract

We present a new concept of modelling energy-using behaviour of residential customers by the use of linguistic variables and a fuzzy logic approach. Trapezoidal- and delta-shaped fuzzy filters were applied to transfer uncertainties of customer declarations expressed in linguistic variables into parameters of structural models. This approach involves individual treatment of each customer. In surveys of energy consumption, precise description of appliance uses is practically impossible. Thus, fuzzy models are appropriate. The method is especially useful for small- and mid-size samples of customers who are connected to the same supply feeder or living in a chosen area.

Suggested Citation

  • Michalik, G. & Khan, M.E. & Bonwick, W.J. & Mielczarski, W., 1997. "Structural modelling of energy demand in the residential sector: 2. The use of linguistic variables to include uncertainty of customers' behaviour," Energy, Elsevier, vol. 22(10), pages 949-958.
    • Handle: RePEc:eee:energy:v:22:y:1997:i:10:p:949-958
    DOI: 10.1016/S0360-5442(97)00030-3
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    Cited by:

    1. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    2. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    3. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
    4. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    5. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    6. Hawkes, Adam & Leach, Matthew, 2005. "Impacts of temporal precision in optimisation modelling of micro-Combined Heat and Power," Energy, Elsevier, vol. 30(10), pages 1759-1779.
    7. Kucukali, Serhat & Baris, Kemal, 2010. "Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach," Energy Policy, Elsevier, vol. 38(5), pages 2438-2445, May.
    8. Egelioglu, F. & Mohamad, A.A. & Guven, H., 2001. "Economic variables and electricity consumption in Northern Cyprus," Energy, Elsevier, vol. 26(4), pages 355-362.
    9. Doukas, Haris, 2013. "Modelling of linguistic variables in multicriteria energy policy support," European Journal of Operational Research, Elsevier, vol. 227(2), pages 227-238.

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