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Topical Section: Artificial Intelligence in Smart Buildings

On-policy learning-based deep reinforcement learning assessment for building control efficiency and stability

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Pages 1150-1165 | Received 14 Sep 2021, Accepted 23 Jun 2022, Published online: 15 Aug 2022

References

  • Andersson, C., J. Åkesson, and C. Führer. 2016. Pyfmi: A python package for simulation of coupled dynamic models with the functional mock-up interface. Lund, Sweden: Centre for Mathematical Sciences, Lund University.
  • ASHRAE Guideline 36P. 2018. ASHRAE guideline 36P: High performance sequences of operation for HVAC systems. Standard. Atlanta GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.
  • Chen, B., Z. Cai, and M. Bergés. 2019. Gnu-rl: A precocial reinforcement learning solution for building hvac control using a differentiable mpc policy. In Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 316–25.
  • Crawley, D. B., L. K. Lawrie, F. C. Winkelmann, W. F. Buhl, Y. Huang, C. O. Pedersen, R. K. Strand, R. J. Liesen, D. E. Fisher, M. J. Witte, et al. 2001. EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings 33 (4):319–31. doi:10.1016/S0378-7788(00)00114-6
  • Deru, M., K. Field, D. Studer, K. Benne, B. Griffith, P. Torcellini, B. Liu, et al. 2011. US Department of Energy commercial reference building models of the national building stock.
  • Ding, X., Du, W., and A. Cerpa. 2019. Octopus: Deep reinforcement learning for holistic smart building control. In Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 326–35.
  • Drgoňa, J., D. Picard, and L. Helsen. 2020. Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration. Journal of Process Control 88:63–77. doi:10.1016/j.jprocont.2020.02.007
  • Drgoňa, J., J. Arroyo, I. C. Figueroa, D. Blum, K. Arendt, D. Kim, E. Perarnau Ollé, et al. 2020. All you need to know about model predictive control for buildings. Annual Reviews in Control 50:190–232.
  • Goel, S., M. Rosenberg, R. Athalye, Y. Xie, W. Wang, R. Hart, J. Zhang, et al. 2014. Enhancements to ASHRAE standard 90.1 prototype building models. Technical Report. Pacific Northwest National Laboratory, Richland, WA.
  • Han, M., R. May, X. Zhang, X. Wang, S. Pan, D. Yan, Y. Jin, and L. Xu. 2019. A review of reinforcement learning methodologies for controlling occupant comfort in buildings. Sustainable Cities and Society 51:101748. http://www.sciencedirect.com/science/article/pii/S2210670719307589. doi:10.1016/j.scs.2019.101748
  • Hill, A., A. Raffin, M. Ernestus, A. Gleave, A. Kanervisto, R. Traore, P. Dhariwal, et al. 2018. Stable baselines. https://github.com/hill-a/stable-baselines.
  • Huang, S., Y. Lin, V. Chinde, X. Ma, and J. Lian. 2021. Simulation-based performance evaluation of model predictive control for building energy systems. Applied Energy 281:116027. doi:10.1016/j.apenergy.2020.116027
  • Ibarz, J., J. Tan, C. Finn, M. Kalakrishnan, P. Pastor, and S. Levine. 2021. How to train your robot with deep reinforcement learning: Lessons we have learned. The International Journal of Robotics Research 40 (4–5):698–721. doi:10.1177/0278364920987859
  • “International Energy Outlook 2019.” 2019. https://www.eia.gov/outlooks/archive/ieo19/pdf/ieo2019.pdf.
  • Jiru, T. E. 2014. Combining HVAC energy conservation measures to achieve energy savings over standard requirements. Energy and Buildings 73:171–5. doi:10.1016/j.enbuild.2014.01.009
  • Kingma, D. P., and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv Preprint arXiv:1412.6980.
  • Kiran, B. R., I. Sobh, V. Talpaert, P. Mannion, A. A. A. Sallab, S. Yogamani, and P. Perez. 2022. Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems 23 (6):4909–26. doi:10.1109/TITS.2021.3054625
  • Lample, G., and D. S. Chaplot. 2017. Playing FPS games with deep reinforcement learning. In 31st AAAI Conference on Artificial Intelligence. doi:10.1609/aaai.v31i1.10827
  • Lee, J.-Y., S. Huang, A. Rahman, A. D. Smith, and S. Katipamula. 2020. Flexible reinforcement learning framework for building control using EnergyPlus-Modelica energy models. In Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities, 34–38.
  • Li, Y. 2017. Deep reinforcement learning: An overview. arXiv Preprint arXiv:1701.07274.
  • Lillicrap, T. P., J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv Preprint arXiv:1509.02971.
  • Mnih, V., A. Puigdomenech Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning, 1928–1937. PMLR.
  • Mnih, V., K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. 2015. Human-level control through deep reinforcement learning. Nature 518 (7540):529–33. doi:10.1038/nature14236
  • Mocanu, E., D. C. Mocanu, P. H. Nguyen, A. Liotta, M. E. Webber, M. Gibescu, and J. G. Slootweg. 2019. On-line building energy optimization using deep reinforcement learning. IEEE Transactions on Smart Grid 10 (4):3698–708. doi:10.1109/TSG.2018.2834219
  • Nouidui, T., M. Wetter, and W. Zuo. 2014. Functional mock-up unit for co-simulation import in EnergyPlus. Journal of Building Performance Simulation 7 (3):192–202. doi:10.1080/19401493.2013.808265
  • Schreiber, T., S. Eschweiler, M. Baranski, and D. Müller. 2020. Application of two promising Reinforcement Learning algorithms for load shifting in a cooling supply system. Energy and Buildings 229:110490. doi:10.1016/j.enbuild.2020.110490
  • Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. 2017. Proximal policy optimization algorithms. arXiv Preprint arXiv:1707.06347.
  • Schulman, J., S. Levine, P. Abbeel, M. Jordan, and P. Moritz. 2015. Trust region policy optimization. In International Conference on Machine Learning, 1889–1897. PMLR.
  • Silver, D., A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529 (7587):484–9. doi:10.1038/nature16961
  • Silver, D., G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller. 2014. Deterministic policy gradient algorithms. In International Conference on Machine Learning, 387–395. PMLR.
  • Silver, D., J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al. 2017. Mastering the game of go without human knowledge. Nature 550 (7676):354–9. doi:10.1038/nature24270
  • Sutton, R. S., and A. G. Barto. 2018. Reinforcement learning: An introduction. MIT press.
  • “Tracking Buildings 2020.” 2020. https://www.iea.org/reports/tracking-buildings-2020.
  • Vázquez-Canteli, J. R., and Z. Nagy. 2019. Reinforcement learning for demand response: A review of algorithms and modeling techniques. Applied Energy 235:1072–89. doi:10.1016/j.apenergy.2018.11.002
  • Verhelst, J., G. Van Ham, D. Saelens, and L. Helsen. 2017. Economic impact of persistent sensor and actuator faults in concrete core activated office buildings. Energy and Buildings 142:111–27. doi:10.1016/j.enbuild.2017.02.052
  • Vinyals, O., I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, et al. 2019. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 575 (7782):350–4. doi:10.1038/s41586-019-1724-z
  • Wang, N., S. Goel, A. Makhmalbaf, and N. Long. 2018. Development of building energy asset rating using stock modelling in the USA. Journal of Building Performance Simulation 11 (1):4–18. doi:10.1080/19401493.2015.1134668
  • Wang, Z., and T. Hong. 2020. Reinforcement learning for building controls: The opportunities and challenges. Applied Energy 269:115036. doi:10.1016/j.apenergy.2020.115036
  • Wei, T., Y. Wang, and Q. Zhu. 2017. Deep reinforcement learning for building HVAC control. In Proceedings of the 54th Annual Design Automation Conference 2017, 1–6.
  • Zhang, Z., A. Chong, Y. Pan, C. Zhang, and K. P. Lam. 2019. Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning. Energy and Buildings 199:472–90. doi:10.1016/j.enbuild.2019.07.029
  • Zhao, J., B. Lasternas, K. P. Lam, R. Yun, and V. Loftness. 2014. Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining. Energy and Buildings 82:341–55. doi:10.1016/j.enbuild.2014.07.033

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