IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v335y2025ics0360544225039192.html

Application of cloud model - Bayesian network and genetic algorithm for assessment and optimization of building energy consumption

Author

Listed:
  • Luo, Wenhong
  • Liu, Weicheng
  • Xia, Lingyu
  • Zheng, Junjun
  • Liu, Yang

Abstract

Building energy consumption (BEC) accounts for a large proportion of total global energy consumption, and has a huge prospect of energy conservation. In order to reduce building energy consumption, it is necessary to effectively evaluate the energy consumption level and optimize it. Due to the mutual influence of multiple factors on building energy consumption, there are often complex interactions and relationships between these factors, making the evaluation and optimization of building energy consumption a complex and uncertain problem. Therefore this paper aims to combine the cloud model (CM)-Bayesian network (BN) and genetic algorithm (GA) to build a comprehensive assessment and optimization model to solve this problem. First a comprehensive and applicable assessment indicator system and rating criteria for BEC is constructed. Second, the advantages of CM in reasoning multi-index complex uncertainty problems are utilized, and the quantitative value is converted into qualitative probability by BN to better solve the randomness and fuzziness of evaluation. Subsequently, the results of Bayesian inference analysis are used to determine the BEC optimization indexes and targets, and creatively inverse the index target values through the probability distribution of the index target level. Then, we selected a teaching building of a university in Wuhan for case validation and successfully assessment the BEC level using the model. Finally, based on the rating results and key indicators, GA is applied to quantitatively optimize the BEC indicators. Our models contribute to the accurate assessment and efficient optimization of BEC, thereby accelerating energy savings in the building sector.

Suggested Citation

  • Luo, Wenhong & Liu, Weicheng & Xia, Lingyu & Zheng, Junjun & Liu, Yang, 2025. "Application of cloud model - Bayesian network and genetic algorithm for assessment and optimization of building energy consumption," Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225039192
    DOI: 10.1016/j.energy.2025.138277
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225039192
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.138277?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Yang & Pedrycz, Witold & Deveci, Muhammet & Chen, Zhen-Song, 2024. "BIM-based building performance assessment of green buildings - A case study from China," Applied Energy, Elsevier, vol. 373(C).
    2. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Li-Yuan Huang & Xun Zhang & Qiang Wang & Zhen-Song Chen & Yang Liu, 2024. "Evaluating Media Knowledge Capabilities of Intelligent Search Dialogue Systems: A Case Study of ChatGPT and New Bing," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 17284-17307, December.
    4. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    5. Hosseini, Farzad & Safari, Amin & Farrokhifar, Meisam, 2020. "Cloud theory-based multi-objective feeder reconfiguration problem considering wind power uncertainty," Renewable Energy, Elsevier, vol. 161(C), pages 1130-1139.
    6. Zhu, Yi & Xu, Wen & Luo, Wenhong & Yang, Ming & Chen, Hongyu & Liu, Yang, 2025. "Application of hybrid machine learning algorithm in multi-objective optimization of green building energy efficiency," Energy, Elsevier, vol. 316(C).
    7. Robinson, Caleb & Dilkina, Bistra & Hubbs, Jeffrey & Zhang, Wenwen & Guhathakurta, Subhrajit & Brown, Marilyn A. & Pendyala, Ram M., 2017. "Machine learning approaches for estimating commercial building energy consumption," Applied Energy, Elsevier, vol. 208(C), pages 889-904.
    8. Kamel, Ehsan & Memari, Ali M., 2018. "Automated Building Energy Modeling and Assessment Tool (ABEMAT)," Energy, Elsevier, vol. 147(C), pages 15-24.
    9. Nimmanterdwong, Prathana & Chalermsinsuwan, Benjapon & Piumsomboon, Pornpote, 2023. "Optimizing utilization pathways for biomass to chemicals and energy by integrating emergy analysis and particle swarm optimization (PSO)," Renewable Energy, Elsevier, vol. 202(C), pages 1448-1459.
    10. Luo, Wenhong & Liu, Weicheng & Liu, Wenlong & Xia, Lingyu & Zheng, Junjun & Liu, Yang, 2025. "Analysis of influencing factors and carbon emission scenario prediction during building operation stage," Energy, Elsevier, vol. 316(C).
    11. Canh, Nguyen Phuc & Schinckus, Christophe & Thanh, Su Dinh & Chong, Felicia Hui Ling, 2021. "The determinants of the energy consumption: A shadow economy-based perspective," Energy, Elsevier, vol. 225(C).
    12. 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.
    13. Chen, Hongyu & Shen, Geoffrey Qiping & Li, Xinyi & Xu, Wen & Liu, Yang, 2025. "Intelligent optimal design for near-zero energy buildings performance: A case study of five climate zones in China," Energy, Elsevier, vol. 333(C).
    14. Ascione, Fabrizio & Bianco, Nicola & Maria Mauro, Gerardo & Napolitano, Davide Ferdinando, 2019. "Building envelope design: Multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. Application to different Italian climatic zones," Energy, Elsevier, vol. 174(C), pages 359-374.
    15. Salata, Ferdinando & Ciancio, Virgilio & Dell'Olmo, Jacopo & Golasi, Iacopo & Palusci, Olga & Coppi, Massimo, 2020. "Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms," Applied Energy, Elsevier, vol. 260(C).
    16. Xu, Wen & Wu, Xianguo & Xiong, Shishu & Li, Tiejun & Liu, Yang, 2025. "Optimizing the sustainable performance of public buildings: A hybrid machine learning algorithm," Energy, Elsevier, vol. 320(C).
    17. Wu, Xianguo & Li, Xinyi & Qin, Yawei & Xu, Wen & Liu, Yang, 2023. "Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions," Applied Energy, Elsevier, vol. 339(C).
    18. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    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. Kong, Xiangfei & Zhao, Caimeng & Deng, Yuzhu & Dai, Huageng & Xu, Bowen & Yuan, Jianjuan, 2026. "A synergistic angle-material-season strategy enabling thermochromic spectrally-selective envelopes for building year-round passive thermal regulation," Energy, Elsevier, vol. 342(C).

    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. Xu, Wen & Wu, Xianguo & Xiong, Shishu & Li, Tiejun & Liu, Yang, 2025. "Optimizing the sustainable performance of public buildings: A hybrid machine learning algorithm," Energy, Elsevier, vol. 320(C).
    2. Chen, Hongyu & Shen, Geoffrey Qiping & Li, Xinyi & Xu, Wen & Liu, Yang, 2025. "Intelligent optimal design for near-zero energy buildings performance: A case study of five climate zones in China," Energy, Elsevier, vol. 333(C).
    3. Zhu, Yi & Xu, Wen & Luo, Wenhong & Yang, Ming & Chen, Hongyu & Liu, Yang, 2025. "Application of hybrid machine learning algorithm in multi-objective optimization of green building energy efficiency," Energy, Elsevier, vol. 316(C).
    4. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).
    5. Yang, Yizhou & Duan, Qiuhua & Samadi, Forooza, 2025. "A systematic review of building energy performance forecasting approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 223(C).
    6. 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).
    7. 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.
    8. Luo, Wenhong & Liu, Weicheng & Liu, Wenlong & Xia, Lingyu & Zheng, Junjun & Liu, Yang, 2025. "Analysis of influencing factors and carbon emission scenario prediction during building operation stage," Energy, Elsevier, vol. 316(C).
    9. dos Santos Ferreira, Greicili & Martins dos Santos, Deilson & Luciano Avila, Sérgio & Viana Luiz Albani, Vinicius & Cardoso Orsi, Gustavo & Cesar Cordeiro Vieira, Pedro & Nilson Rodrigues, Rafael, 2023. "Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios," Applied Energy, Elsevier, vol. 339(C).
    10. Muhammad Altaf & Wesam Salah Alalaoul & Muhamamad Ali Musarat & Abdelaziz Abdelmahmoud Abdelaziz & Muhammad Jamaluddin Thaheem, 2024. "Optimisation of energy and life cycle costs via building envelope: a BIM approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(3), pages 7105-7128, March.
    11. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    12. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
    13. Fath U Min Ullah & Noman Khan & Tanveer Hussain & Mi Young Lee & Sung Wook Baik, 2021. "Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework," Mathematics, MDPI, vol. 9(6), pages 1-22, March.
    14. Yesilyurt, Hasan & Dokuz, Yesim & Dokuz, Ahmet Sakir, 2024. "Data-driven energy consumption prediction of a university office building using machine learning algorithms," Energy, Elsevier, vol. 310(C).
    15. Shamim Akhtar & Muhamad Zahim Bin Sujod & Syed Sajjad Hussain Rizvi, 2022. "An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms," Energies, MDPI, vol. 15(15), pages 1-19, August.
    16. Somu, Nivethitha & Raman M R, Gauthama & Ramamritham, Krithi, 2021. "A deep learning framework for building energy consumption forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    17. 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.
    18. 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).
    19. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    20. 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).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:energy:v:335:y:2025:i:c:s0360544225039192. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.