Modelling of residential energy consumption at the national level

Aydinalp M., Ugursal V., Fung A.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, cilt.27, sa.4, ss.441-453, 2003 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 4
  • Basım Tarihi: 2003
  • Doi Numarası: 10.1002/er.887
  • Dergi Adı: INTERNATIONAL JOURNAL OF ENERGY RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.441-453
  • Anahtar Kelimeler: energy, modelling, residential energy consumption, ARTIFICIAL NEURAL NETWORKS, CONDITIONAL-DEMAND, FUEL SUBSTITUTION, CANADA, EFFICIENCY, IMPACT
  • Hacettepe Üniversitesi Adresli: Hayır

Özet

Three methods are currently used to model residential energy consumption at the national level: the engineering method (EM), the conditional demand analysis (CDA) method, and the neural network (NN) method. While the use of the first two methods has been established over the past decade for residential energy modelling, the use of NN method is still in the development and verification phase.

Three methods are currently used to model residential energy consumption at the national level: the engineering method (EM), the conditional demand analysis (CDA) method, and the neural network (NN) method. While the use of the first two methods has been established over the past decade for residentialenergy modelling, the use of NN method is still in the development and verification phase. 

The EM involves developing a housing database representative of the national housing stock and estimating the energy consumption of the dwellings in thedatabase using a building energy simulation program. CDA is a regression-based method in which the regression attributes consumption to end-uses on thebasis of the total household energy consumption. The NN method models the residential energy consumption as a neural network, which is an information-processing model inspired by the way the densely interconnected, parallel structure of the brain processes information. 

In this paper, the three methods are briefly described and a comparative assessment of the three methods is presented.