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Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique

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Author Info

  • Hou, Zhijian
  • Lian, Zhiwei
  • Yao, Ye
  • Yuan, Xinjian
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    Abstract

    A novel method integrating rough sets (RS) theory and an artificial neural network (ANN) based on data-fusion technique is presented to forecast an air-conditioning load. Data-fusion technique is the process of combining multiple sensors data or related information to estimate or predict entity states. In this paper, RS theory is applied to find relevant factors to the load, which are used as inputs of an artificial neural-network to predict the cooling load. To improve the accuracy and enhance the robustness of load forecasting results, a general load-prediction model, by synthesizing multi-RSAN (MRAN), is presented so as to make full use of redundant information. The optimum principle is employed to deduce the weights of each RSAN model. Actual prediction results from a real air-conditioning system show that, the MRAN forecasting model is better than the individual RSAN and moving average (AMIMA) ones, whose relative error is within 4%. In addition, individual RSAN forecasting results are better than that of ARIMA.

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    Bibliographic Info

    Article provided by Elsevier in its journal Applied Energy.

    Volume (Year): 83 (2006)
    Issue (Month): 9 (September)
    Pages: 1033-1046

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    Handle: RePEc:eee:appene:v:83:y:2006:i:9:p:1033-1046

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    Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description

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    Related research

    Keywords: Cooling-load forecasting Rough sets Artificial neural-networks Data-fusion technique;

    References

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    1. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    2. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
    3. Li, Renpu & Wang, Zheng-ou, 2004. "Mining classification rules using rough sets and neural networks," European Journal of Operational Research, Elsevier, vol. 157(2), pages 439-448, September.
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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. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, Open Access Journal, vol. 5(11), pages 4497-4516, November.
    3. 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.
    4. Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.

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