IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p5981-d1531540.html
   My bibliography  Save this article

Transfer Learning for the Prediction of Energy Performance of Water-Cooled Electric Chillers: Grey-Box Models Versus Deep Neural Network (DNN) Models

Author

Listed:
  • Hongwen Dou

    (Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Gina Cody School of Engineering and Computer Science, Montreal, QC H3G 1M8, Canada)

  • Radu Zmeureanu

    (Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Gina Cody School of Engineering and Computer Science, Montreal, QC H3G 1M8, Canada)

Abstract

The development of data-driven prediction models of energy performance of HVAC equipment, such as chillers, depends on the quality and quantity of measurement data for the model training. The practical applications always struggle with the credibility of results when the training dataset of an existing chiller is relatively small. Moreover, when the energy analyst needs to develop a reliable predictive model of a new chiller, the manufacturer’s proprietary data are not always available. The transfer learning method can soften these constraints and can help in the development of a predictive model that captures the knowledge from the available chiller, called the source chiller, using a small dataset, and apply it to a new chiller, called the target chiller. The paper presents the successful application of transfer learning strategies by using grey-box models and DNN models for the prediction of chillers performance, when measurement data are recorded at 15 min time intervals by the building automation system (BAS) and used for training and testing. The paper confirms the initial hypothesis that both the grey-box models and DNN models of the source chiller from July 2013 predict well the energy performance of the target chiller with measurement datasets from 2016. The DNN models perform slightly better than the grey-box models. The pre-trained grey-box models and DNN models, respectively, are transferred to the target chiller using three strategies: SelfL, TLS0, and TLS1, and the results are compared. SelfL strategy trains and tests the models only with the target data. TLS0 strategy directly transfers the models from the source chiller to the target chiller. TLS1 strategy transfers the models, pre-trained with an extended dataset that is composed of training dataset of D s and training dataset of D t . Finally, the models are tested with another set of testing data. The difference in computation times of these two types of models is not significant for preventing the use of DNN models for the applications within the BAS, when compared with grey-box models.

Suggested Citation

  • Hongwen Dou & Radu Zmeureanu, 2024. "Transfer Learning for the Prediction of Energy Performance of Water-Cooled Electric Chillers: Grey-Box Models Versus Deep Neural Network (DNN) Models," Energies, MDPI, vol. 17(23), pages 1-11, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5981-:d:1531540
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/5981/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/23/5981/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hongwen Dou & Radu Zmeureanu, 2023. "Transfer Learning Prediction Performance of Chillers for Neural Network Models," Energies, MDPI, vol. 16(20), pages 1-16, October.
    2. Sarran, Lucile & Smith, Kevin M. & Hviid, Christian A. & Rode, Carsten, 2022. "Grey-box modelling and virtual sensors enabling continuous commissioning of hydronic floor heating," Energy, Elsevier, vol. 261(PB).
    3. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2021. "Feature assessment frameworks to evaluate reduced-order grey-box building energy models," Applied Energy, Elsevier, vol. 298(C).
    4. Qian, Fanyue & Gao, Weijun & Yang, Yongwen & Yu, Dan, 2020. "Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption," Energy, Elsevier, vol. 193(C).
    5. Liu, Jiaquan & Hou, Lei & Zhang, Rui & Sun, Xingshen & Yu, Qiaoyan & Yang, Kai & Zhang, Xinru, 2023. "Explainable fault diagnosis of oil-gas treatment station based on transfer learning," Energy, Elsevier, vol. 262(PA).
    6. Yuan, Yue & Chen, Zhihua & Wang, Zhe & Sun, Yifu & Chen, Yixing, 2023. "Attention mechanism-based transfer learning model for day-ahead energy demand forecasting of shopping mall buildings," Energy, Elsevier, vol. 270(C).
    7. Qiang, Guofeng & Tang, Shu & Hao, Jianli & Di Sarno, Luigi & Wu, Guangdong & Ren, Shaoxing, 2023. "Building automation systems for energy and comfort management in green buildings: A critical review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(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. Li, Guannan & Wu, Yubei & Yoon, Sungmin & Fang, Xi, 2024. "Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning," Energy, Elsevier, vol. 299(C).
    2. Li, Guannan & Zhan, Lei & Fang, Xi & Gao, Jiajia & Xu, Chengliang & He, Xin & Deng, Jiahui & Xiong, Chenglong, 2024. "Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: Data generation, incremental learning, transfer learning, and physics-info," Energy, Elsevier, vol. 312(C).
    3. Zhang, Chengyu & Ma, Liangdong & Luo, Zhiwen & Han, Xing & Zhao, Tianyi, 2024. "Forecasting building plug load electricity consumption employing occupant-building interaction input features and bidirectional LSTM with improved swarm intelligent algorithms," Energy, Elsevier, vol. 288(C).
    4. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    5. Chen, Bingyang & Zeng, Xingjie & Zhang, Weishan & Fan, Lulu & Cao, Shaohua & Zhou, Jiehan, 2023. "Knowledge sharing-based multi-block federated learning for few-shot oil layer identification," Energy, Elsevier, vol. 283(C).
    6. Xue, Wenyuan & Lu, Yichen & Wang, Zhi & Cao, Shengxian & Sui, Mengxuan & Yang, Yuan & Li, Jiyuan & Xie, Yubin, 2024. "Reconstructing near-water-wall temperature in coal-fired boilers using improved transfer learning and hidden layer configuration optimization," Energy, Elsevier, vol. 294(C).
    7. Jiang, Ben & Li, Yu & Rezgui, Yacine & Zhang, Chengyu & Wang, Peng & Zhao, Tianyi, 2024. "Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings," Energy, Elsevier, vol. 299(C).
    8. Yuhong Zhao & Ruirui Liu & Zhansheng Liu & Liang Liu & Jingjing Wang & Wenxiang Liu, 2023. "A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    9. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(C).
    10. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    11. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
    12. Li, Jiangkuan & Lin, Meng & Li, Yankai & Wang, Xu, 2022. "Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions," Energy, Elsevier, vol. 254(PB).
    13. Jiaxi Hu & Chengxi Lyu & Yinzhen Hou & Neng Zhu & Kairui Liu, 2024. "Research on Summer Indoor Air Conditioning Design Parameters in Haikou City: A Field Study of Indoor Thermal Perception and Comfort," Sustainability, MDPI, vol. 16(9), pages 1-17, May.
    14. Tang, Lingfeng & Xie, Haipeng & Wang, Xiaoyang & Bie, Zhaohong, 2023. "Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach," Applied Energy, Elsevier, vol. 337(C).
    15. Hu, Chenxi & Zhang, Jun & Yuan, Hongxia & Gao, Tianlu & Jiang, Huaiguang & Yan, Jing & Wenzhong Gao, David & Wang, Fei-Yue, 2022. "Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19," Applied Energy, Elsevier, vol. 309(C).
    16. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
    17. Lu, Yakai & Tian, Zhe & Zhang, Qiang & Zhou, Ruoyu & Chu, Chengshan, 2021. "Data augmentation strategy for short-term heating load prediction model of residential building," Energy, Elsevier, vol. 235(C).
    18. Xiao, Tianqi & You, Fengqi, 2024. "Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities," Applied Energy, Elsevier, vol. 353(PB).
    19. Zhang, Limao & Guo, Jing & Lin, Penghui & Tiong, Robert L.K., 2025. "Detecting energy consumption anomalies with dynamic adaptive encoder-decoder deep learning networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    20. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, 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:jeners:v:17:y:2024:i:23:p:5981-:d:1531540. 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.