Physically Consistent Neural Networks for building thermal modeling: Theory and analysis

Citations

Cited by:

1. Guo, Yanhua & Wang, Ningbo & Shao, Shuangquan & Huang, Congqi & Zhang, Zhentao & Li, Xiaoqiong & Wang, Youdong, 2024. "A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
2. Guo, Fangzhou & Li, Ao & Yue, Bao & Xiao, Ziwei & Xiao, Fu & Yan, Rui & Li, Anbang & Lv, Yan & Su, Bing, 2024. "Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network," Applied Energy, Elsevier, vol. 354(PA).
3. Montazeri, Mina & Remlinger, Carl & Bejar Haro, Benjamin & Heer, Philipp, 2025. "Fully data-driven and modular building thermal control with physically consistent modeling," Applied Energy, Elsevier, vol. 390(C).
4. Sha, Peng & Zhang, Yao & Wu, Xiao & Wang, Meihong & Shen, Jiong, 2025. "Modeling of propane thermal cracking process via physics-informed neural networks with process-consistent constraints," Energy, Elsevier, vol. 333(C).
5. Chen, Dong & Chui, Chee-Kong & Lee, Poh Seng, 2025. "Adaptive physically consistent neural networks for data center thermal dynamics modeling," Applied Energy, Elsevier, vol. 377(PD).
6. Karim Boubouh & Robert Basmadjian & Omid Ardakanian & Alexandre Maurer & Rachid Guerraoui, 2023. "PePTM : An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling," Energies, MDPI, vol. 16(18), pages 1-22, September.
7. Chen, Dong & Chui, Chee-Kong & Lee, Poh Seng, 2026. "Physics informed machine learning based predictive control for intelligent operation of edge datacenters," Applied Energy, Elsevier, vol. 402(PB).
8. Taboga, Vincent & Gehring, Clement & Cam, Mathieu Le & Dagdougui, Hanane & Bacon, Pierre-Luc, 2024. "Neural differential equations for temperature control in buildings under demand response programs," Applied Energy, Elsevier, vol. 368(C).
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. 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).
11. Ma, Zhihao & Jiang, Gang & Hu, Yuqing & Chen, Jianli, 2025. "A review of physics-informed machine learning for building energy modeling," Applied Energy, Elsevier, vol. 381(C).
12. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.
13. 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).
14. Avisek Naug & Marcos Quinones-Grueiro & Gautam Biswas, 2025. "An End-to-End Relearning Framework for Building Energy Optimization," Energies, MDPI, vol. 18(6), pages 1-23, March.
15. Hagemann, Willem & Weichmann, Jaßper & Gernandt, Hannes & Krenzlin, Franziska & Schiffer, Johannes, 2026. "Modeling and optimization of borehole thermal energy storage systems using physics-based neural networks," Renewable Energy, Elsevier, vol. 256(PA).
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. Zhang, Qingang & Zeng, Wei & Lin, Qinjie & Chng, Chin-Boon & Chui, Chee-Kong & Lee, Poh-Seng, 2023. "Deep reinforcement learning towards real-world dynamic thermal management of data centers," Applied Energy, Elsevier, vol. 333(C).
18. 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).
19. Jiang, Fuyang & Kazmi, Hussain, 2025. "What-if: A causal machine learning approach to control-oriented modelling for building thermal dynamics," Applied Energy, Elsevier, vol. 377(PC).
20. Engel, Jens & Schmitt, Thomas & Rodemann, Tobias & Adamy, Jürgen, 2024. "Hierarchical MPC for building energy management: Incorporating data-driven error compensation and mitigating information asymmetry," Applied Energy, Elsevier, vol. 372(C).
21. Ebbs-Picken, Takiah & Romero, David A. & Da Silva, Carlos M. & Amon, Cristina H., 2024. "Deep encoder–decoder hierarchical convolutional neural networks for conjugate heat transfer surrogate modeling," Applied Energy, Elsevier, vol. 372(C).
22. Liang, Xinbin & Chen, Siliang & Mao, Zhuyun & Li, Xilin & Jin, Xinqiao & Du, Zhimin, 2025. "Physics-informed neural network-based model predictive control for chiller plant – fan coil unit system and intelligent human-AI interaction via large language model," Energy, Elsevier, vol. 339(C).
23. 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).
24. Liang, Xinbin & Liu, Ying & Chen, Siliang & Li, Xilin & Jin, Xinqiao & Du, Zhimin, 2025. "Physics-informed neural network for chiller plant optimal control with structure-type and trend-type prior knowledge," Applied Energy, Elsevier, vol. 390(C).
25. Mokhtari, Reza & Montazeri, Mina & Cai, Hanmin & Heer, Philipp & Li, Rongling, 2025. "Price-responsive control using deep reinforcement learning for heating systems: Simulation and living lab experiment," Energy, Elsevier, vol. 337(C).
26. Silvestri, Alberto & Coraci, Davide & Brandi, Silvio & Capozzoli, Alfonso & Borkowski, Esther & Köhler, Johannes & Wu, Duan & Zeilinger, Melanie N. & Schlueter, Arno, 2024. "Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control," Applied Energy, Elsevier, vol. 368(C).