IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v342y2026ics0360544225054027.html

Heat loss identification method for heating building exterior walls based on BO-TCN-LSTM-SA

Author

Listed:
  • Zhang, Dongjie
  • Mei, Yong
  • Wang, Xiaoyang
  • Wang, Zheng
  • Cheng, Yun
  • Zhan, Changhong

Abstract

To address the limitations of current energy monitoring methods for heating buildings—such as strict testing conditions, long cycles, and low efficiency, which hinder accurate characterization of the dynamic thermal behavior of exterior walls—this study proposes a grey-box deep learning model, BO-TCN-LSTM-SA, optimized via Bayesian techniques. The model integrates wall heat transfer mechanisms and correlation analysis, with input variables including indoor and outdoor environmental parameters, wall surface temperatures, meteorological factors, and historical data. A case study on a residential building in Harbin tested four different input configurations. The results showed that the Case 2 input scheme achieved the best balance between identification accuracy and practical applicability, with an average error of 2.21 W/m2 and a relative error of 7.0 %. Compared with the traditional LSTM model, the proposed model reduced RMSE and MAE by 25.0 % and 20.2 %, respectively, and improved R2 by 6.4 %. This method enables efficient and accurate identification of the surface heat flux on heating building envelopes and provides a reliable basis for energy retrofit diagnostics and decisions in cold climate regions.

Suggested Citation

  • Zhang, Dongjie & Mei, Yong & Wang, Xiaoyang & Wang, Zheng & Cheng, Yun & Zhan, Changhong, 2026. "Heat loss identification method for heating building exterior walls based on BO-TCN-LSTM-SA," Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:energy:v:342:y:2026:i:c:s0360544225054027
    DOI: 10.1016/j.energy.2025.139759
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225054027
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.139759?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. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
    2. Martín Pensado-Mariño & Lara Febrero-Garrido & Estibaliz Pérez-Iribarren & Pablo Eguía Oller & Enrique Granada-Álvarez, 2021. "Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks," Energies, MDPI, vol. 14(16), pages 1-14, August.
    3. Bienvenido-Huertas, David & Rubio-Bellido, Carlos & Solís-Guzmán, Jaime & Oliveira, Miguel José, 2020. "Experimental characterisation of the periodic thermal properties of walls using artificial intelligence," Energy, Elsevier, vol. 203(C).
    4. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).
    5. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    6. Bienvenido-Huertas, David & Moyano, Juan & Rodríguez-Jiménez, Carlos E. & Marín, David, 2019. "Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method," Applied Energy, Elsevier, vol. 233, pages 1-14.
    7. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    8. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    9. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    10. Pan, Shaowei & Yang, Bo & Wang, Shukai & Guo, Zhi & Wang, Lin & Liu, Jinhua & Wu, Siyu, 2023. "Oil well production prediction based on CNN-LSTM model with self-attention mechanism," Energy, Elsevier, vol. 284(C).
    Full references (including those not matched with items on IDEAS)

    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. Stefenon, Stefano Frizzo & Seman, Laio Oriel & da Silva, Evandro Cardozo & Finardi, Erlon Cristian & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Hypertuned wavelet convolutional neural network with long short-term memory for time series forecasting in hydroelectric power plants," Energy, Elsevier, vol. 313(C).
    2. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    3. Liu, Jingtao & Zhang, Yuxiang & Wang, Yixian & Zhang, Hongbin & Ding, Yunfei, 2026. "A novel multi-model operation mechanism based on Gaussian classification and data decomposition for air-conditioning cooling load prediction in complex public buildings," Energy, Elsevier, vol. 347(C).
    4. Peng, Shiliang & Fan, Lin & Zhang, Li & Su, Huai & He, Yuxuan & He, Qian & Wang, Xiao & Yu, Dejun & Zhang, Jinjun, 2024. "Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network," Energy, Elsevier, vol. 301(C).
    5. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    6. Zhao, Xiaoyu & Duan, Pengfei & Cao, Xiaodong & Xue, Qingwen & Zhao, Bingxu & Hu, Jinxue & Zhang, Chenyang & Yuan, Xiaoyang, 2025. "A probabilistic load forecasting method for multi-energy loads based on inflection point optimization and integrated feature screening," Energy, Elsevier, vol. 327(C).
    7. Xu, Huifeng & Hu, Feihu & Liang, Xinhao & Zhao, Guoqing & Abugunmi, Mohammad, 2024. "A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network," Energy, Elsevier, vol. 299(C).
    8. Luo, Run & Li, Yadong & Guo, Huiyu & Wang, Qi & Wang, Xiaolie, 2024. "Cross-operating-condition fault diagnosis of a small module reactor based on CNN-LSTM transfer learning with limited data," Energy, Elsevier, vol. 313(C).
    9. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    10. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    11. David Bienvenido-Huertas, 2020. "Assessing the Environmental Impact of Thermal Transmittance Tests Performed in Façades of Existing Buildings: The Case of Spain," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    12. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
    13. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    14. Rahman, Aowabin & Smith, Amanda D., 2018. "Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms," Applied Energy, Elsevier, vol. 228(C), pages 108-121.
    15. Huang, Jingjing & Zhou, Wenjuan & Fan, Ying & Wang, Maofa, 2026. "Multivariate transfer entropy-guided self-attention for nonlinear causal interaction modeling: Evidence from energy systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 681(C).
    16. Xiaoyu Liu & Jiangfeng Song & Hai Tao & Peng Wang & Haihua Mo & Wenjie Du, 2025. "Quarter-Hourly Power Load Forecasting Based on a Hybrid CNN-BiLSTM-Attention Model with CEEMDAN, K-Means, and VMD," Energies, MDPI, vol. 18(11), pages 1-29, May.
    17. Chen, Haoyu & Huang, Hai & Zheng, Yong & Yang, Bing, 2024. "A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model," Applied Energy, Elsevier, vol. 375(C).
    18. Cui, Xuyang & Zhu, Junda & Jia, Lifu & Wang, Jiahui & Wu, Yusen, 2024. "A novel heat load prediction model of district heating system based on hybrid whale optimization algorithm (WOA) and CNN-LSTM with attention mechanism," Energy, Elsevier, vol. 312(C).
    19. Wang, Jun & Cao, Junxing, 2024. "Reservoir properties inversion using attention-based parallel hybrid network integrating feature selection and transfer learning," Energy, Elsevier, vol. 304(C).
    20. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(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:342:y:2026:i:c:s0360544225054027. 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.