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All issues Volume 562 (2024) E3S Web of Conf., 562 (2024) 06003 References
Open Access
Issue
E3S Web of Conf.
Volume 562, 2024
BuildSim Nordic 2024
Article Number 06003
Number of page(s) 16
Section System Optimization and Building Performance Simulation (BPS)
DOI https://doi.org/10.1051/e3sconf/202456206003
Published online 07 August 2024
  1. M. Santamouris and K. Vasilakopoulou, Present and future energy consumption of buildings: Challenges and opportunities towards decarbonization, E-Prime Adv. Electr. Eng. Electron. Energy, vol. 1, p. 100002 (2021).https://doi: 10.1016/j.prime.2021.100002 [CrossRef] [Google Scholar]
  2. J. G. Boldt et al., ASHRAE Guideline 36-2021 (2021). https://www.ashrae.org/ [Google Scholar]
  3. S. T. Taylor, Degrading Chilled Water Plant Delta-T: Causes and Mitigation. ASHRAE Transactions, AC-02-6-1 (2002). [Google Scholar]
  4. M. Trčka and J. L. M. Hensen, Overview of HVAC system simulation, Autom. Constr., vol. 19, no. 2, pp. 93–99 (2010). https://doi:10.1016/j.autcon.2009.11.019 [CrossRef] [Google Scholar]
  5. L. Wang, E. W. M. Lee, and R. K. K. Yuen, A practical approach to chiller plants’ optimization, Energy Build., vol. 169, pp. 332–343 (2018). https://doi:10.1016/j.enbuild.2018.03.076 [CrossRef] [Google Scholar]
  6. K. Zaw, K. Matsuoka, and T. K. Poh, A real-time operational optimization of commercial district cooling systems: A practical application, Energy Build., vol. 297, p. 113434 (2023). https://doi:10.1016/j.enbuild.2023.113434 [CrossRef] [Google Scholar]
  7. H. S. Sane, C. Haugstetter, and S. A. Bortoff, Building HVAC control systems role of controls and optimization, in 2006 American Control Conference, Minneapolis, MN, USA: IEEE p. 6 pp. (2006). https://doi:10.1109/ACC.2006.1656367 [Google Scholar]
  8. S. Qiu, Z. Li, D. Fan, R. He, X. Dai, and Z. Li, Chilled water temperature resetting using model-free reinforcement learning: Engineering application, Energy Build., vol. 255, p. 111694 (2022). https://doi:10.1016/j.enbuild.2021.111694 [CrossRef] [Google Scholar]
  9. D. Zhang, P. B. Luh, J. Fan, and S. Gupta, Chiller Plant Operation Optimization: EnergyEfficient Primary-Only and Primary–Secondary Systems, IEEE Trans. Autom. Sci. Eng., vol. 15, no. 1, pp. 341–355 (2018). https://doi:10.1109/TASE.2017.2751605 [CrossRef] [Google Scholar]
  10. Y. Sun, S. Wang, and G. Huang, Chiller sequencing control with enhanced robustness for energy-efficient operation, Energy Build., vol. 41, no. 11, pp. 1246–1255, (2009). https://doi:10.1016/j.enbuild.2009.07.023 [CrossRef] [Google Scholar]
  11. Z. Zheng et al., Data-Driven Chiller Sequencing for Reducing HVAC Electricity Consumption in Commercial Buildings, in Proceedings of the Ninth International Conference on Future Energy Systems, in e-Energy ’18. New York, NY, USA: Association for Computing Machinery, pp. 236–248 (2018). https://doi:10.1145/3208903.3208913 [Google Scholar]
  12. H. S. Asad, R. K. K. Yuen, and G. Huang, Degree of freedom based setpoint reset scheme for HVAC real-time optimization, Energy Build., vol. 128, pp. 349–359 (2016). https://doi:10.1016/j.enbuild.2016.07.013 [CrossRef] [Google Scholar]
  13. Y. Zhang, R. Li, F. Wang, A. Li, D. Huang, and G. Chen, Rule-based optimal control for the cooling water subsystem of chiller plants, IECON 2017 43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, 2017, pp. 8138-8143. https://doi:10.1109/IECON.2017.8217428 [Google Scholar]
  14. S. A. R. Naqvi et al., Data-Driven Co-optimization of Energy Efficiency and Indoor Environmental Quality in Commercial Buildings, presented at the e-Energy 2023 Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, 2023, pp. 140–144. https://doi:10.1145/3599733.3600262 [Google Scholar]
  15. T. Kannan et al., Energy Management Strategy for Zone Cooling Load Demand Reduction in Commercial Buildings: A Data-Driven Approach, IEEE Transactions on Industry Applications, vol. 55, no. 6, pp. 7281-7299, (2019). https://ieeexplore.ieee.org/document/8769859 [CrossRef] [Google Scholar]
  16. D. Zhuang, V. J. L. Gan, Z. Duygu Tekler, A. Chong, S. Tian, and X. Shi, “Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning,” Appl. Energy, vol. 338 (2023). https://doi:10.1016/j.apenergy.2023.120936 [CrossRef] [Google Scholar]
  17. Z. Li and J. Zhang, “Data-oriented distributed overall optimization for large-scale HVAC systems with dynamic supply capability and distributed demand response, Build. Environ., vol. 221 (2022). https://doi:10.1016/j.buildenv.2022.109322 [Google Scholar]
  18. S. Wang and Z. Ma, Supervisory and optimal control of building HVAC systems: A review, HVAC R Res., vol. 14, no. 1, pp. 3–32 (2008). https://doi:10.1080/10789669.2008.10390991 [CrossRef] [Google Scholar]
  19. Z. Zhang, A. Chong, Y. Pan, C. Zhang, and K. P. Lam, Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning, Energy Build., vol. 199, pp. 472–490, (2019). https://doi:10.1016/j.enbuild.2019.07.029 [CrossRef] [Google Scholar]
  20. E. Sala-Cardoso, M. Delgado-Prieto, K. Kampouropoulos, and L. Romeral, Predictive chiller operation: A data-driven loading and scheduling approach, Energy Build., vol. 208 (2020) https://doi:10.1016/j.enbuild.2019.109639 [CrossRef] [Google Scholar]
  21. R. De Coninck, R. Baetens, B. Verbruggen, J. Driesen, D. Saelens, and L. Helsen, Modelling and simulation of a grid-connected photovoltaic heat pump system with thermal energy storage using Modelica, in 8th International Conference on System Simulation in Buildings (SSB2010), P. Andre, S. Bertagnolio, and V. Lemort, Eds., Liège, 2010, P177 [Google Scholar]
  22. M. Ala’raj, M. Radi, M. F. Abbod, M. Majdalawieh, and M. Parodi, Data-driven based HVAC optimization approaches: A Systematic Literature Review, J. Build. Eng., vol. 46, p. 103678 (2022) https://doi:10.1016/j.jobe.2021.103678 [CrossRef] [Google Scholar]
  23. CASE STUDY EXECUTIVE SUMMARY Chiller optimisation at MaiaShopping cools down costs (2017) https://www.sonaesierra.com/case-studies/ [Google Scholar]
  24. Demand Flow® Chilled Water System Optimization Intelligent.Powerful.Proven.usa.siemens.com/demandflow ©Siemens Industry, Inc. 2018 [Google Scholar]
  25. H. G. Kim and S. S. Kim, Development of Energy Benchmarks for Office Buildings Using the National Energy Consumption Database. Energies Journal, MDPI, vol. 13(4), 950 (2020). [Google Scholar]
  26. H. S. Park, M. Lee, K. Hyuna, T. Hong, J. Jaewook,(2016), Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques. Applied Energy, Elsevier s, vol. 173, pp. 225–237. [Google Scholar]
  27. A. Ferrantelli, J. Belikov, E. Petlenkov, M. Thalfeldt, and J. Kurnitski, Evaluating the Energy Readiness of National Building Stocks Through Benchmarking, IEEE Access, vol. 10, pp. 45430–45443 (2022). https://doi:10.1109/ACCESS.2022.3170716 [CrossRef] [Google Scholar]

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