IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v4y2011i3p475-487d11640.html
   My bibliography  Save this article

Analysis of a Residential Building Energy Consumption Demand Model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/4/3/475/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/4/3/475/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Crompton, Paul & Wu, Yanrui, 2005. "Energy consumption in China: past trends and future directions," Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
    2. Saha, G.P. & Stephenson, J., 1980. "A model of residential energy use in New Zealand," Energy, Elsevier, vol. 5(2), pages 167-175.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    2. Yu, Wei & Li, Baizhan & Yang, Xincheng & Wang, Qingqin, 2015. "A development of a rating method and weighting system for green store buildings in China," Renewable Energy, Elsevier, vol. 73(C), pages 123-129.
    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.
    4. Bing Xiao & Xuexiu Jia & Dong Yang & Lingwen Sun & Feng Shi & Qitong Wang & Yongfei Jia, 2022. "Research on Classification Method of Building Function Oriented to Urban Building Stock Management," Sustainability, MDPI, vol. 14(10), pages 1-13, May.
    5. Hyun-Kil Jo & Hye-Mi Park & Jin-Young Kim, 2019. "Carbon Offset Service and Design Guideline of Tree Planting for Multifamily Residential Sites in Korea," Sustainability, MDPI, vol. 11(13), pages 1-14, June.
    6. Lefeng Cheng & Zhiyi Zhang & Haorong Jiang & Tao Yu & Wenrui Wang & Weifeng Xu & Jinxiu Hua, 2018. "Local Energy Management and Optimization: A Novel Energy Universal Service Bus System Based on Energy Internet Technologies," Energies, MDPI, vol. 11(5), pages 1-38, May.
    7. David Bienvenido-Huertas & Carlos Rubio-Bellido & Juan Luis Pérez-Ordóñez & Fernando Martínez-Abella, 2019. "Estimating Adaptive Setpoint Temperatures Using Weather Stations," Energies, MDPI, vol. 12(7), pages 1-47, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    3. Marcin Zygmunt & Dariusz Gawin, 2021. "Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock," Energies, MDPI, vol. 14(24), pages 1-15, December.
    4. Wang, Manyu & Wei, Chu, 2024. "Toward sustainable heating: Assessment of the carbon mitigation potential from residential heating in northern rural China," Energy Policy, Elsevier, vol. 190(C).
    5. Edyta Ropuszyńska-Surma & Magdalena Węglarz, 2016. "Residential electricity consumption in Poland," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 26(3), pages 69-82.
    6. Guarino, Francesco & Cassarà, Pietro & Longo, Sonia & Cellura, Maurizio & Ferro, Erina, 2015. "Load match optimisation of a residential building case study: A cross-entropy based electricity storage sizing algorithm," Applied Energy, Elsevier, vol. 154(C), pages 380-391.
    7. Lyu, Xingbao & Yuan, Yi & Ning, Wenjing & Chen, Li & Tao, Wen-Quan, 2024. "Investigation and optimization of PEMFC-CHP systems based on Chinese residential thermal and electrical consumption data," Applied Energy, Elsevier, vol. 356(C).
    8. 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.
    9. Yanxia Li & Chao Wang & Sijie Zhu & Junyan Yang & Shen Wei & Xinkai Zhang & Xing Shi, 2020. "A Comparison of Various Bottom-Up Urban Energy Simulation Methods Using a Case Study in Hangzhou, China," Energies, MDPI, vol. 13(18), pages 1-23, September.
    10. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    11. Bloch, Harry & Rafiq, Shuddhasattwa & Salim, Ruhul, 2015. "Economic growth with coal, oil and renewable energy consumption in China: Prospects for fuel substitution," Economic Modelling, Elsevier, vol. 44(C), pages 104-115.
    12. Weiqiang Zhu & Yun Zhang, 2024. "Household Energy Clean Transition Mechanisms under Market Failures: A Government Financing Perspective," Sustainability, MDPI, vol. 16(13), pages 1-29, July.
    13. Damianakis, Nikolaos & Mouli, Gautham Ram Chandra & Bauer, Pavol & Yu, Yunhe, 2023. "Assessing the grid impact of Electric Vehicles, Heat Pumps & PV generation in Dutch LV distribution grids," Applied Energy, Elsevier, vol. 352(C).
    14. Zhang, Yi & Ji, Qiang & Fan, Ying, 2018. "The price and income elasticity of China's natural gas demand: A multi-sectoral perspective," Energy Policy, Elsevier, vol. 113(C), pages 332-341.
    15. Xu, Bin & Lin, Boqiang, 2018. "Do we really understand the development of China's new energy industry?," Energy Economics, Elsevier, vol. 74(C), pages 733-745.
    16. Omar Shafqat & Elena Malakhatka & Nina Chrobot & Per Lundqvist, 2021. "End Use Energy Services Framework Co-Creation with Multiple Stakeholders—A Living Lab-Based Case Study," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    17. Zhang, Yuyang & Ma, Wenke & Du, Pengcheng & Li, Shaoting & Gao, Ke & Wang, Yuxuan & Liu, Yifei & Zhang, Bo & Yu, Dingyi & Zhang, Jingyi & Li, Yan, 2024. "Powering the future: Unraveling residential building characteristics for accurate prediction of total electricity consumption during summer heat," Applied Energy, Elsevier, vol. 376(PA).
    18. Dergiades, Theologos & Tsoulfidis, Lefteris, 2008. "Estimating residential demand for electricity in the United States, 1965-2006," Energy Economics, Elsevier, vol. 30(5), pages 2722-2730, September.
    19. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    20. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:4:y:2011:i:3:p:475-487:d:11640. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.