IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i4p2444-d754173.html
   My bibliography  Save this article

A Novel Method of BP Neural Network Based Green Building Design—The Case of Hotel Buildings in Hot Summer and Cold Winter Region of China

Author

Listed:
  • Bin Xu

    (China Architecture Design & Research Group, Beijing 100044, China
    School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Xiang Yuan

    (School of Civil Engineering, North China University of Technology, Beijing 100144, China)

Abstract

With the advent of the big data era, architectural design gradually tends to become more quantified and intelligent. This study proposes a novel green design method for energy-saving buildings based on a BP neural network. This study changed the traditional trial–error mode by evaluating energy consumption based on design performance parameters such as building shape, space, and interface. Instead, energy consumption quota values obtained from statistical data, as well as thermal parameters and energy system parameters in energy-saving standards, were taken as input parameters, and then the design scheme of building shape can be obtained through BP neural network technology. Based on data of 61 hotel buildings in a representative city among a hot summer and cold winter climate zone, the BP neural network model is established to control the building design variables, with 41 kgce/m 2 ·a as its energy-saving design target. Through the energy consumption quota, the trained BP network is applied to predict the optimal architectural design parameters, including the building orientation angle, shape coefficient, window–wall ratio, etc., for twelve building typologies in an area range of 5000~60,000 m 2 . With recommended control thresholds of quantifiable architectural design elements obtained, this research can provide effective design decision-making suggestions for architects.

Suggested Citation

  • Bin Xu & Xiang Yuan, 2022. "A Novel Method of BP Neural Network Based Green Building Design—The Case of Hotel Buildings in Hot Summer and Cold Winter Region of China," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2444-:d:754173
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/4/2444/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/4/2444/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qi Dong & Kai Xing & Hongrui Zhang, 2017. "Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions," Sustainability, MDPI, vol. 10(1), pages 1-15, December.
    2. Méndez Echenagucia, Tomás & Capozzoli, Alfonso & Cascone, Ylenia & Sassone, Mario, 2015. "The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis," Applied Energy, Elsevier, vol. 154(C), pages 577-591.
    3. Mechri, Houcem Eddine & Capozzoli, Alfonso & Corrado, Vincenzo, 2010. "USE of the ANOVA approach for sensitive building energy design," Applied Energy, Elsevier, vol. 87(10), pages 3073-3083, October.
    4. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
    5. A.M. Fogheri, 2015. "Energy Efficiency in Public Buildings," Rivista economica del Mezzogiorno, Società editrice il Mulino, issue 3-4, pages 763-784.
    6. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    7. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    8. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    9. Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), 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. Enci Liu & Jie Li & Anni Zheng & Haoran Liu & Tao Jiang, 2022. "Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network," Sustainability, MDPI, vol. 14(15), pages 1-19, July.

    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. Hou, D. & Evins, R., 2024. "A protocol for developing and evaluating neural network-based surrogate models and its application to building energy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    2. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    3. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2014. "A novel dynamic modeling approach for predicting building energy performance," Applied Energy, Elsevier, vol. 114(C), pages 91-103.
    4. Delgarm, N. & Sajadi, B. & Kowsary, F. & Delgarm, S., 2016. "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)," Applied Energy, Elsevier, vol. 170(C), pages 293-303.
    5. Li, Hong Xian & Li, Yan & Jiang, Boya & Zhang, Limao & Wu, Xianguo & Lin, Jingyi, 2020. "Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis," Renewable Energy, Elsevier, vol. 149(C), pages 1414-1423.
    6. Xue, Peng & Li, Qian & Xie, Jingchao & Zhao, Mengjing & Liu, Jiaping, 2019. "Optimization of window-to-wall ratio with sunshades in China low latitude region considering daylighting and energy saving requirements," Applied Energy, Elsevier, vol. 233, pages 62-70.
    7. Chen, Xi & Yang, Hongxing & Sun, Ke, 2016. "A holistic passive design approach to optimize indoor environmental quality of a typical residential building in Hong Kong," Energy, Elsevier, vol. 113(C), pages 267-281.
    8. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    9. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    10. Nan Wang & Daniel Satola & Aoife Houlihan Wiberg & Conghong Liu & Arild Gustavsen, 2020. "Reduction Strategies for Greenhouse Gas Emissions from High-Speed Railway Station Buildings in a Cold Climate Zone of China," Sustainability, MDPI, vol. 12(5), pages 1-22, February.
    11. Huang, Junchao & Chen, Xi & Yang, Hongxing & Zhang, Weilong, 2018. "Numerical investigation of a novel vacuum photovoltaic curtain wall and integrated optimization of photovoltaic envelope systems," Applied Energy, Elsevier, vol. 229(C), pages 1048-1060.
    12. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
    13. Shilei Lu & Zichen Wang & Tianshuai Zhang, 2020. "Quantitative Analysis and Multi-Index Evaluation of the Green Building Envelope Performance in the Cold Area of China," Sustainability, MDPI, vol. 12(1), pages 1-38, January.
    14. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    15. Kostevšek, Anja & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Papa, Gregor & Petek, Janez, 2016. "The concept of an ecosystem model to support the transformation to sustainable energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1460-1469.
    16. Leehter Yao & Jin-Hao Huang, 2019. "Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center," Energies, MDPI, vol. 12(8), pages 1-16, April.
    17. Khan, Mohd Shariq & Lee, Moonyong, 2013. "Design optimization of single mixed refrigerant natural gas liquefaction process using the particle swarm paradigm with nonlinear constraints," Energy, Elsevier, vol. 49(C), pages 146-155.
    18. Yu Dong & Tongyu Qin & Siyuan Zhou & Lu Huang & Rui Bo & Haibo Guo & Xunzhi Yin, 2020. "Comparative Whole Building Life Cycle Assessment of Energy Saving and Carbon Reduction Performance of Reinforced Concrete and Timber Stadiums—A Case Study in China," Sustainability, MDPI, vol. 12(4), pages 1-24, February.
    19. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    20. 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.

    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:jsusta:v:14:y:2022:i:4:p:2444-:d:754173. 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.