Multi-Model Predictive Control on HVAC Chilled Water Pump for Temperature Control and Energy Consumption Reduction for Auditorium

MUHAMMAD AFIFF RIZKI, ARIES SUBIANTORO

Abstract


Most energy consumption comes from the high demand from buildings. A large portion from buildings comes from the HVAC systems. A proper optimal control strategy is needed for energy savings. This paper proposes a multi-model MPC strategy for controlling the chilled water pump HVAC to reduce energy consumption for an auditorium in MAC UI building. The building model is modeled in EnergyPlus software, while the control strategy is modeled using MATLAB where both software communicates using BCVTB. The developed control performance has been tested under different operating conditions and different set of temperature changes. The result shows that the proposed controller can reduce the total energy consumption of chilled water pump by 13.1% compared to the existing On/Off controller.


Keywords


BCVTB; Chilled Water Pump; HVAC; MPC; Optimal Control

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References


Anil, S., Srinaath, H. H., & Jayakumar, M. (2023). Comparative Analysis of ON/OFF, PID and Model Predictive Control System in HVAC. 2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023. doi: 10.1109/I-PACT58649.2023.10434750

ASHRAE. (2010). Thermal Environmental Conditions for Human Occupancy. Retrieved from www.ashrae.org

Camacho, E. F., & Carlos, B. (2007). Model Predictive Control Advanced Textbooks in Control and Signal Processing.

Cong, G., Wang, R., & Wang, D. (2024). Application of Internet of Things in Distributed HVAC Control. 2024 11th International Forum on Electrical Engineering and Automation, IFEEA 2024, 1083–1086. doi: 10.1109/IFEEA64237.2024.10878723

Eid, E., Foster, A., Alvarez, G., Ndoye, F. T., Leducq, D., & Evans, J. (2024). Modelling energy consumption in a Paris supermarket to reduce energy use and greenhouse gas emissions using EnergyPlus. International Journal of Refrigeration, 168, 1–8. doi: 10.1016/j.ijrefrig.2024.08.023

Fons, J. D. V., & Paya, J. (2024). HVAC system operation, consumption and compressor size optimization in urban buses of Mediterranean cities. Energy, 296. doi: 10.1016/j.energy.2024.131151

Henze, G. P., Kircher, K. J., & Braun, J. E. (2024). Why has advanced commercial HVAC control not yet achieved its promise? Journal of Building Performance Simulation. doi: 10.1080/19401493.2024.2429728

Homod, R. Z. (2018). Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings. Renewable Energy, 126, 49–64. doi: 10.1016/j.renene.2018.03.022

Leon, M. (2025). Why are advanced HVAC control systems important in commercial buildings? - Consulting - Specifying Engineer. Retrieved from https://www.csemag.com/why-are-advanced-hvac-control-systems-important-incommercial-buildings/

Lestanto, Y., Subiantoro, A., & Yusivar, F. (2017). Two-stage Least Square Method for Model Identification of Vehicle Motion. IEEE.

Mantovani, G., & Ferrarini, L. (2015). Temperature Control of a Commercial Building with Model Predictive Control Techniques. IEEE Transactions on Industrial Electronics, 62(4), 2651–2660. doi: 10.1109/TIE.2014.2387095

Meteonorm. (n.d.). Retrieved from https://meteonorm.com/en/

Morales, L., Aguilar, J., Garces-Jimenez, A., Gutierrez De Mesa, J. A., & Gomez-Pulido, J. M. (2020). Advanced fuzzy-logic-based context-driven control for HVAC management systems in buildings. IEEE Access, 8, 16111–16126. doi: 10.1109/ACCESS.2020.2966545

Nishijima, D. (2016). Product lifetime, energy efficiency and climate change: A case study of air conditioners in Japan. Journal of Environmental Management, 181, 582–589. doi: 10.1016/j.jenvman.2016.07.010

Nouidui, T. (2016). FrontPage - bcvtb. Retrieved from https://simulationresearch.lbl.gov/bcvtb/FrontPage

Radecki, P., & Hencey, B. (2017). Online Model Estimation for Predictive Thermal Control of Buildings. IEEE Transactions on Control Systems Technology, 25(4), 1414–1422. doi: 10.1109/TCST.2016.2587737

U.S Department of Energy. (2019). EnergyPlusTM Version 9.2.0 Documentation.

Valenzuela, P. E., Ebadat, A., Everitt, N., & Parisio, A. (2020). Closed-loop identification for model predictive control of HVAC systems: From input design to controller synthesis. IEEE Transactions on Control Systems Technology, 28(5), 1681–1695. doi: 10.1109/TCST.2019.2917675

Wijaya, T. K., Sholahudin, Idrus Alhamid, M., Saito, K., & Nasruddin, N. (2022). Dynamic optimization of chilled water pump operation to reduce HVAC energy consumption. Thermal Science and Engineering Progress, 36. doi: 10.1016/j.tsep.2022.101512

Yang, Y., Hu, G., & Spanos, C. J. (2022). Stochastic Optimal Control of HVAC System for Energy-Efficient Buildings. IEEE Transactions on Control Systems Technology, 30(1), 376–383. doi: 10.1109/TCST.2021.3057630

Ziabari, M. T., Jahed-Motlagh, M. R., Salahshoor, K., Ramezani, A., & Moarefianpur, A. (2017). Robust adaptive control of surge instability in constant speed centrifugal compressors using tube-MPC. Cogent Engineering, 4(1). doi: 10.1080/23311916.2017.1339335




DOI: https://doi.org/10.26760/elkomika.v13i3.271

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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

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Department of Electrical Engineering Institut Teknologi Nasional Bandung, Indonesia

Address: 20th Building  Institut Teknologi Nasional Bandung PHH. Mustofa Street No. 23 Bandung 40124, Indonesia

Contact: +627272215 (ext. 206)

Email: jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________


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