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


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  • Aydinalp, Merih
  • Ismet Ugursal, V.
  • Fung, Alan S.
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Applied Energy.

    Volume (Year): 71 (2002)
    Issue (Month): 2 (February)
    Pages: 87-110

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    Handle: RePEc:eee:appene:v:71:y:2002:i:2:p:87-110

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    Keywords: Residential energy consumption modeling Appliance; lighting; and space-cooling energy Neural networks modeling;


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    Cited by:
    1. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    2. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    3. Yu, Biying & Zhang, Junyi & Fujiwara, Akimasa, 2011. "Representing in-home and out-of-home energy consumption behavior in Beijing," Energy Policy, Elsevier, vol. 39(7), pages 4168-4177, July.
    4. Thiaw, L. & Sow, G. & Fall, S.S. & Kasse, M. & Sylla, E. & Thioye, S., 2010. "A neural network based approach for wind resource and wind generators production assessment," Applied Energy, Elsevier, vol. 87(5), pages 1744-1748, May.
    5. Ermis, K. & Midilli, A. & Dincer, I. & Rosen, M.A., 2007. "Artificial neural network analysis of world green energy use," Energy Policy, Elsevier, vol. 35(3), pages 1731-1743, March.
    6. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    7. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.


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