Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks


Aydinalp M. , Ugursal V., Fung A.

APPLIED ENERGY, cilt.71, ss.87-110, 2002 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 71 Konu: 2
  • Basım Tarihi: 2002
  • Doi Numarası: 10.1016/s0306-2619(01)00049-6
  • Dergi Adı: APPLIED ENERGY
  • Sayfa Sayıları: ss.87-110

Özet

Two methods are currently used to model residential energy consumption at the national or regional level: the engineering method and the conditional demand analysis method. Another potentially feasible method to model residential energy consumption is the neural network (NN) method. Using the NN method, it is possible to determine causal relationships amongst a large number of parameters. such as occur in the energy consumption patterns in the residential sector. A review of the published literature indicates that the NN method has not been used or tested for housing-sector energy consumption modeling. A NN based energy consumption model is being developed for the Canadian residential sector. This paper presents the NN methodology used in developing the appliances, lighting, and space-cooling component of the model, the accuracy of its predictions, and some sample results. (C) 2002 Elsevier Science Ltd. All rights reserved.

Two methods are currently used to model residential energy consumption at the national or regional level: the engineering method and the conditional demand analysis method. Another potentially feasible method to model residential energy consumption is the neural network (NN) method. Using the NN method, it is possible to determine causal relationships amongst a large number of parameters. such as occur in the energy consumption patterns in the residential sector. A review of the published literature indicates that the NN method has not been used or tested for housing-sector energy consumption modeling. A NN based energy consumption model is being developed for the Canadian residential sector. This paper presents the NN methodology used in developing the appliances, lighting, and space-cooling component of the model, the accuracy of its predictions, and some sample results.