IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5766-d1523812.html
   My bibliography  Save this article

A Review of Building Physical Shapes on Heating and Cooling Energy Consumption

Author

Listed:
  • Jin Li

    (School of Civil Engineering, Sichuan University of Science & Engineering, Zigong 643000, China)

  • Chao Liang

    (School of Civil Engineering, Sichuan University of Science & Engineering, Zigong 643000, China)

  • Wenwu Zhou

    (College of Chemistry and Environment Engineering, Sichuan University of Science & Engineering, Zigong 643000, China)

Abstract

The shape of a building profoundly impacts its energy consumption throughout its life and is a critical consideration in early architectural design. Despite its significance, the influence of building shape on heating and air conditioning energy usage remains insufficiently understood. This study systematically analyzes the relationship between building shape and energy consumption, grounded in objective facts about building energy performance from the perspective of architects during the initial design phases. This analysis aids designers in making informed decisions. Key parameters, notably the widely used building shape coefficient, are examined. The relationship between building shape and energy consumption across various global and China’s diverse climate zones is synthesized. Current simulation tools and methodologies are assessed to guide future research. Findings reveal a predominant reliance on simulations for comparing energy use across specific building shapes. The academic understanding of the shape−energy relationship remains superficial, complicating standardization. Future research should prioritize extensive, multi-parameter simulations to enhance understanding of building performance, thereby facilitating energy-efficient design.

Suggested Citation

  • Jin Li & Chao Liang & Wenwu Zhou, 2024. "A Review of Building Physical Shapes on Heating and Cooling Energy Consumption," Energies, MDPI, vol. 17(22), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5766-:d:1523812
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5766/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/22/5766/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Liu & Lam, Joseph C. & Tsang, C.L., 2008. "Energy performance of building envelopes in different climate zones in China," Applied Energy, Elsevier, vol. 85(9), pages 800-817, September.
    2. Chi, Fang'ai & Xu, Ying, 2022. "Building performance optimization for university dormitory through integration of digital gene map into multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 307(C).
    3. Tamás Storcz & Zsolt Ercsey & Kristóf Roland Horváth & Zoltán Kovács & Balázs Dávid & István Kistelegdi, 2023. "Energy Design Synthesis: Algorithmic Generation of Building Shape Configurations," Energies, MDPI, vol. 16(5), pages 1-17, February.
    4. Singh, Manav Mahan & Singaravel, Sundaravelpandian & Geyer, Philipp, 2021. "Machine learning for early stage building energy prediction: Increment and enrichment," Applied Energy, Elsevier, vol. 304(C).
    5. Wang, Ran & Feng, Wei & Wang, Lan & Lu, Shilei, 2021. "A comprehensive evaluation of zero energy buildings in cold regions: Actual performance and key technologies of cases from China, the US, and the European Union," Energy, Elsevier, vol. 215(PA).
    6. Shiyi Song & Hong Leng & Han Xu & Ran Guo & Yan Zhao, 2020. "Impact of Urban Morphology and Climate on Heating Energy Consumption of Buildings in Severe Cold Regions," IJERPH, MDPI, vol. 17(22), pages 1-24, November.
    7. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    8. Premrov, Miroslav & Žigart, Maja & Žegarac Leskovar, Vesna, 2018. "Influence of the building shape on the energy performance of timber-glass buildings located in warm climatic regions," Energy, Elsevier, vol. 149(C), pages 496-504.
    9. Shilei Lu & Xiaolei Tang & Liran Ji & Daixin Tu, 2017. "Research on Energy-Saving Optimization for the Performance Parameters of Rural-Building Shape and Envelope by TRNSYS-GenOpt in Hot Summer and Cold Winter Zone of China," Sustainability, MDPI, vol. 9(2), pages 1-18, February.
    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. Nabil Matari & Abdelkader Mahi & Nesrine Chabane & Zouaoui R. Harrat & Marijana Hadzima-Nyarko, 2025. "Design Methodology Development for High-Energy-Efficiency Buildings in Algerian Sahara Climatic Context," Sustainability, MDPI, vol. 17(6), pages 1-27, March.

    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. Zhaoxia Wang & Jing Zhao, 2018. "Optimization of Passive Envelop Energy Efficient Measures for Office Buildings in Different Climate Regions of China Based on Modified Sensitivity Analysis," Sustainability, MDPI, vol. 10(4), pages 1-28, March.
    2. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    3. Kazimierz Kawa & Rafał Mularczyk & Waldemar Bauer & Katarzyna Grobler-Dębska & Edyta Kucharska, 2024. "Prediction of Energy Consumption on Example of Heterogenic Commercial Buildings," Energies, MDPI, vol. 17(13), pages 1-16, June.
    4. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    5. 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).
    6. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    7. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    8. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    9. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Jiang-Jiang, Wang & Chun-Fa, Zhang & You-Yin, Jing, 2010. "Multi-criteria analysis of combined cooling, heating and power systems in different climate zones in China," Applied Energy, Elsevier, vol. 87(4), pages 1247-1259, April.
    11. Suzana Domjan & Sašo Medved & Boštjan Černe & Ciril Arkar, 2019. "Fast Modelling of nZEB Metrics of Office Buildings Built with Advanced Glass and BIPV Facade Structures," Energies, MDPI, vol. 12(16), pages 1-18, August.
    12. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    13. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    14. Ouyang, F.Y. & Zheng, B. & Jiang, X.F., 2014. "Spatial and temporal structures of four financial markets in Greater China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 236-244.
    15. Mao, Ning & Hao, Jingyu & He, Tianbiao & Song, Mengjie & Xu, Yingjie & Deng, Shiming, 2019. "PMV-based dynamic optimization of energy consumption for a residential task/ambient air conditioning system in different climate zones," Renewable Energy, Elsevier, vol. 142(C), pages 41-54.
    16. Yushi Wang & Beining Hu & Xianhai Meng & Runjin Xiao, 2024. "A Comprehensive Review on Technologies for Achieving Zero-Energy Buildings," Sustainability, MDPI, vol. 16(24), pages 1-26, December.
    17. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    18. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    19. Sarabia Escriva, Emilio José & Hart, Matthew & Acha, Salvador & Soto Francés, Víctor & Shah, Nilay & Markides, Christos N., 2022. "Techno-economic evaluation of integrated energy systems for heat recovery applications in food retail buildings," Applied Energy, Elsevier, vol. 305(C).
    20. Chun-Wei Chen, 2023. "A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?," Sustainability, MDPI, vol. 15(5), pages 1-21, February.

    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:17:y:2024:i:22:p:5766-:d:1523812. 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.