IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v342y2023ics0306261923005299.html
   My bibliography  Save this article

Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization

Author

Listed:
  • Xiao, Tianqi
  • You, Fengqi

Abstract

Being a primary contributor to global energy consumption and energy-related carbon emissions, the building and building construction sectors are a crucial player in the decarbonization and energy efficiency efforts. This article proposes a novel physically consistent deep learning (PCDL) approach for building thermal modeling and assesses its potential for optimizing building energy efficiency and indoor thermal comfort through model predictive control (MPC). The PCDL model considers physical relationships between system inputs and outputs and is applied to predict the indoor thermal climate. With the strict guarantee of satisfying the laws of physics, the proposed PCDL model has better generalization ability than other machine learning approaches. Subsequently, the PCDL model is integrated into an MPC controller to optimize building energy consumption and indoor thermal comfort. The proposed approach is tested on Carpenter Hall, a multi-zone office building located in Cornell University Campus, through simulations. Based on the simulation results, the PCDL model demonstrate a much better generalization ability for yielding physically-feasible predictions compared to the long short-term memory (LSTM) model. Compared to an On/Off controller, a state space model-based MPC controller, and an LSTM-based MPC controller, the proposed PCDL-based MPC reduces by 5.8%, 4.5%, and 8.9% energy consumption and improves by 55%, 59%, and 64% indoor thermal comfort, respectively, and therefore enhances the building decarbonization progress. The results emphasize the importance of considering physics information in data-driven models and highlight the advantages of the proposed PCDL-based MPC controller.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:342:y:2023:i:c:s0306261923005299
    DOI: 10.1016/j.apenergy.2023.121165
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923005299
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.121165?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    2. Park, June Young & Nagy, Zoltan, 2018. "Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2664-2679.
    3. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    4. Li, Hangxin & Wang, Shengwei, 2022. "Comparative assessment of alternative MPC strategies using real meteorological data and their enhancement for optimal utilization of flexibility-resources in buildings," Energy, Elsevier, vol. 244(PA).
    5. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage," Applied Energy, Elsevier, vol. 327(C).
    6. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    7. Beckman, William A. & Broman, Lars & Fiksel, Alex & Klein, Sanford A. & Lindberg, Eva & Schuler, Mattias & Thornton, Jeff, 1994. "TRNSYS The most complete solar energy system modeling and simulation software," Renewable Energy, Elsevier, vol. 5(1), pages 486-488.
    8. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    9. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    10. Hu, Guoqing & You, Fengqi, 2022. "Renewable energy-powered semi-closed greenhouse for sustainable crop production using model predictive control and machine learning for energy management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    11. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    12. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    13. Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    14. Chen, Wei-Han & You, Fengqi, 2022. "Sustainable building climate control with renewable energy sources using nonlinear model predictive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    15. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2022. "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis," Applied Energy, Elsevier, vol. 325(C).
    16. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    17. 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).
    18. Chen, Wei-Han & Mattson, Neil S. & You, Fengqi, 2022. "Intelligent control and energy optimization in controlled environment agriculture via nonlinear model predictive control of semi-closed greenhouse," Applied Energy, Elsevier, vol. 320(C).
    19. Jayathissa, P. & Luzzatto, M. & Schmidli, J. & Hofer, J. & Nagy, Z. & Schlueter, A., 2017. "Optimising building net energy demand with dynamic BIPV shading," Applied Energy, Elsevier, vol. 202(C), pages 726-735.
    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. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
    2. Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
    3. 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).
    4. Hu, Guoqing & You, Fengqi, 2024. "AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory," Applied Energy, Elsevier, vol. 356(C).
    5. Xu, Wenjie & Svetozarevic, Bratislav & Di Natale, Loris & Heer, Philipp & Jones, Colin N., 2024. "Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach," Applied Energy, Elsevier, vol. 358(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. 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).
    2. Hu, Guoqing & You, Fengqi, 2024. "AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory," Applied Energy, Elsevier, vol. 356(C).
    3. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
    4. 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).
    5. Ajagekar, Akshay & Decardi-Nelson, Benjamin & You, Fengqi, 2024. "Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 355(C).
    6. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2022. "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis," Applied Energy, Elsevier, vol. 325(C).
    7. Chen, Wei-Han & You, Fengqi, 2022. "Sustainable building climate control with renewable energy sources using nonlinear model predictive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
    9. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage," Applied Energy, Elsevier, vol. 327(C).
    10. Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
    11. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    12. Amasyali, Kadir & El-Gohary, Nora M., 2021. "Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort," Applied Energy, Elsevier, vol. 302(C).
    13. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort," Energy, Elsevier, vol. 289(C).
    14. Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
    15. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    16. 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).
    17. Kwok Wai Mui & Ling Tim Wong & Manoj Kumar Satheesan & Anjana Balachandran, 2021. "A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong," Energies, MDPI, vol. 14(16), pages 1-18, August.
    18. Paulína Šujanová & Monika Rychtáriková & Tiago Sotto Mayor & Affan Hyder, 2019. "A Healthy, Energy-Efficient and Comfortable Indoor Environment, a Review," Energies, MDPI, vol. 12(8), pages 1-37, April.
    19. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    20. Svetozarevic, B. & Baumann, C. & Muntwiler, S. & Di Natale, L. & Zeilinger, M.N. & Heer, P., 2022. "Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments," Applied Energy, Elsevier, vol. 307(C).

    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:appene:v:342:y:2023:i:c:s0306261923005299. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.