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Analysis of a Residential Building Energy Consumption Demand Model

Author

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  • Wei Yu

    (Key Lab of the Three Gorges Reservoir Region’s Eco-Environment, Chongqing University, Ministry of Education, Chongqing, 400045, China
    Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing, 400045, China)

  • Baizhan Li

    (Key Lab of the Three Gorges Reservoir Region’s Eco-Environment, Chongqing University, Ministry of Education, Chongqing, 400045, China
    Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing, 400045, China)

  • Yarong Lei

    (Key Lab of the Three Gorges Reservoir Region’s Eco-Environment, Chongqing University, Ministry of Education, Chongqing, 400045, China
    Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing, 400045, China)

  • Meng Liu

    (Key Lab of the Three Gorges Reservoir Region’s Eco-Environment, Chongqing University, Ministry of Education, Chongqing, 400045, China
    Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing, 400045, China)

Abstract

In order to estimate the energy consumption demand of residential buildings, this paper first discusses the status and shortcomings of current domestic energy consumption models. Then it proposes and develops a residential building energy consumption demand model based on a back propagation (BP) neural network model. After that, taking residential buildings in Chongqing (P.R. China) as an example, 16 energy consumption indicators are introduced as characteristics of the residential buildings in Chongqing. The index system of the BP neutral network prediction model is established and the multi-factorial BP neural network prediction model of Chongqing residential building energy consumption is developed using the Cshap language, based on the SQL server 2005 platform. The results obtained by applying the model in Chongqing are in good agreement with actual ones. In addition, the model provides corresponding approximate data by taking into account the potential energy structure adjustments and relevant energy policy regulations.

Suggested Citation

  • Wei Yu & Baizhan Li & Yarong Lei & Meng Liu, 2011. "Analysis of a Residential Building Energy Consumption Demand Model," Energies, MDPI, vol. 4(3), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:3:p:475-487:d:11640
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    References listed on IDEAS

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    3. Bentzen, Jan & Engsted, Tom, 2001. "A revival of the autoregressive distributed lag model in estimating energy demand relationships," Energy, Elsevier, vol. 26(1), pages 45-55.
    4. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
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    Cited by:

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    3. Azadeh Sadeghi & Roohollah Younes Sinaki & William A. Young & Gary R. Weckman, 2020. "An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks," Energies, MDPI, vol. 13(3), pages 1-23, January.
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    8. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.

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