Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO2 with OpenModelica Modeling
Abstract
1. Introduction
1.1. Control Strategies of TC
1.2. Control Strategies of IAQ
2. Background
2.1. Principles of Indoor Air Temperature Control and CO2 Control
2.2. Multi-Control of Indoor Air Temperature and CO2
2.3. OpenModelica Modeling
3. Methodology and Case Study
3.1. Multi-Control Strategies for the Control of Indoor Air Temperaure and CO2
3.1.1. Strategy 1: PID Control for Indoor Air Temperature and Fixed Minimum Outdoor Air Fraction
3.1.2. Strategy 2: PID Control for Indoor Air Temperature and Fixed Outdoor Air Rate
3.1.3. Strategy 3: PID Control for Indoor Air Temperature and Demand-Controlled Outdoor Air Rate
3.2. Case Study
- Scenario 1: PID control of indoor air temperature and fixed outdoor air ratio to dilute CO2 contaminant.
- Scenario 2: PID controls of indoor air temperature and design outdoor air rate to dilute CO2 contaminant.
- Scenario 3: PID controls of indoor air temperature and demanded-control ventilation scheme to dilute CO2 contaminant with Wi-Fi probe enabled occupancy.
- The indoor air temperature and CO2 concentration were uniformly distributed inside the room;
- We assumed there were no natural ventilation of all rooms with good air tightness;
- The heat transfer through the building envelope were ignored, and heat gains for all zones were assumed to come from occupants, lights, and computers;
4. Results of Multi-Control Strategies
4.1. Scenario 1: PID Control of Thermal Comfort and Fixed Outdoor Air Ratio
4.2. Scenario 2: PID Control of Thermal Comfort and Design Outdoor Air Rate
4.3. Scenario 3: PID Control of Thermal Comfort and Demand-Based Ventilation with Wi-Fi-Enabled Occupancy
4.4. The Energy Use of the Three Scenarios
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Pérez-Lombard, L.; Ortiz, J.; Pout, C. A review on buildings energy consumption information. Energy Build. 2008, 40, 394–398. [Google Scholar] [CrossRef]
- Lymperopoulos, G.; Ioannou, P. Building Temperature Regulation in a Multi-Zone HVAC System using Distributed Adaptive Control. Energy Build. 2020, 215, 109825. [Google Scholar] [CrossRef]
- Antoniadou, P.; Papadopoulos, A.M. Occupants’ thermal comfort: State of the art and the prospects of personalized assessment in office buildings. Energy Build. 2007, 153, 136–149. [Google Scholar] [CrossRef]
- Englund, J.S.; Cehlin, M.; Akander, J.; Moshfegh, B. Measured and Simulated Energy Use in a Secondary School Building in Sweden—A Case Study of Validation, Airing, and Occupancy Behavior. Energies 2020, 13, 2325. [Google Scholar] [CrossRef]
- Silva, M.F.; Maas, S.; De Souza, H.A.; Gomes, A.P. Post-occupancy evaluation of residential buildings in Luxembourg with centralized and decentralized ventilation systems, focusing on indoor air quality (IAQ). Assessment by questionnaires and physical measurements. Energy Build. 2017, 148, 119–127. [Google Scholar] [CrossRef]
- Lin, Z.; Chow, T.T.; Fong, K.F.; Tsang, C.F.; Wang, Q. Comparison of performances of displacement and mixing ventilations. Part II: Indoor air quality. Inter. J. Refrig. 2005, 28, 288–305. [Google Scholar] [CrossRef]
- Kim, J.; Song, D.; Kim, S.; Park, S.; Choi, Y.; Lim, H. Energy-Saving Potential of Extending Temperature Set-Points in a VRF Air-Conditioned Building. Energies 2020, 13, 2160. [Google Scholar] [CrossRef]
- Mathews, E.H.; Arndt, D.C.; Piani, C.B.; Van Heerden, E. Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort. Appl. Energy 2000, 66, 135–159. [Google Scholar] [CrossRef]
- Dounis, A.I.; Caraiscos, C. Advanced control systems engineering for energy and comfort management in a building environment-A review. Renew. Sustain. Energy Rev. 2009, 13, 1246–1261. [Google Scholar] [CrossRef]
- Koulani, C.; Hviid, C.; Terkildsen, S. Optimized damper control of pressure and airflow in ventilation systems. In Proceedings of the 10th Nordic Symposium on Building Physics, Lund, Sweden, 15–19 June 2014; pp. 15–19. [Google Scholar]
- Wang, S.; Xu, X. Optimal and robust control of outdoor ventilation airflow rate for improving energy efficiency and IAQ. Build. Environ. 2004, 39, 763–773. [Google Scholar] [CrossRef]
- Shan, X.; Xu, W.; Lee, Y.K.; Lu, W.Z. Evaluation of thermal environment by coupling CFD analysis and wireless-sensor measurements of a full-scale room with cooling system. Sustain. Cities Soc. 2019, 45, 395–405. [Google Scholar] [CrossRef]
- Shan, X.; Luo, N.; Sun, K.; Hong, T.; Lee, Y.K.; Lu, W.Z. Coupling CFD and Building Energy Modelling to Optimize the Operation of a Large Open Office Space for Occupant Comfort. Sustain. Cities Soc. 2020, 60, 102257. [Google Scholar] [CrossRef]
- Yao, Y.; Lian, Z.; Liu, W.; Hou, Z.; Wu, M. Evaluation program for the energy-saving of variable-air-volume systems. Energy Build. 2007, 39, 558–568. [Google Scholar] [CrossRef]
- Okochi, G.S.; Yao, Y. A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems. Renew. Sustain. Energy Rev. 2016, 59, 784–817. [Google Scholar] [CrossRef]
- Zhang, X.; Wang, R.; Xing, J. Optimization control method of VAV air conditioning system. In Proceedings of the 12th World Congress on Intelligent Control and Automation (WCICA), Guilin, China, 12–15 June 2016; pp. 1105–1110. [Google Scholar]
- Tukur, A.; Hallinan, K.P. Statistically informed static pressure control in multiple-zone VAV systems. Energy Build. 2017, 135, 244–252. [Google Scholar] [CrossRef]
- Engdahl, F.; Johansson, D. Optimal supply air temperature with respect to energy use in a variable air volume system. Energy Build. 2004, 36, 205–218. [Google Scholar] [CrossRef]
- Dong, J.; Winstead, C.; Nutaro, J.; Kuruganti, T. Occupancy-based HVAC control with short-term occupancy prediction algorithms for energy-efficient buildings. Energies 2018, 11, 2427. [Google Scholar] [CrossRef]
- Yuan, J.; Chen, Z.; Zhong, L.; Wang, B. Indoor air quality management based on fuzzy risk assessment and its case study. Sustain. Cities Soc. 2019, 50, 101654. [Google Scholar] [CrossRef]
- Griffiths, M.; Eftekhari, M. Control of CO2 in a naturally ventilated classroom. Energy Build. 2008, 40, 556–560. [Google Scholar] [CrossRef]
- ASHRAE Standard. 62.1, 2019. Standard 62.1-2019 Ventilation for Acceptable Indoor Air Quality; American Society of Heating, Refrigerating and Air Conditioning Engineers: Atlanta, GA, USA, 2019. [Google Scholar]
- Chao, C.Y.H.; Hu, J.S. Development of a dual-mode demand control ventilation strategy for indoor air quality control and energy saving. Build. Environ. 2004, 39, 385–397. [Google Scholar] [CrossRef]
- Kumar, P.; Martani, C.; Morawska, L.; Norford, L.; Choudhary, R.; Bell, M.; Leach, M. Indoor air quality and energy management through real-time sensing in commercial buildings. Energy Build. 2016, 111, 145–153. [Google Scholar] [CrossRef]
- Lu, T.; Lü, X.; Viljanen, M. A novel and dynamic demand-controlled ventilation strategy for CO2 control and energy saving in buildings. Energy Build. 2011, 43, 2499–2508. [Google Scholar] [CrossRef]
- Nassif, N.; Kajl, S.; Sabourin, R. Ventilation control strategy using the supply CO2 concentration setpoint. HVAC R Res. 2005, 11, 239–262. [Google Scholar] [CrossRef]
- Wang, W.; Wang, J.; Chen, J.; Huang, G.; Guo, X. Multi-zone outdoor air coordination through Wi-Fi probe-based occupancy sensing. Energy Build. 2018, 159, 495–507. [Google Scholar] [CrossRef]
- ASHRAE Standard. Standard 55–2017 Thermal Environmental Conditions for Human Occupancy; ASHRAE: Atlanta, GA, USA, 2017. [Google Scholar]
- Karunakaran, R.; Iniyan, S.; Goic, R. Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings. Appl. Energy 2010, 87, 1158–1175. [Google Scholar] [CrossRef]
- Pang, X.; Piette, M.A.; Zhou, N. Characterizing variations in variable air volume system controls. Energy Build. 2017, 135, 166–175. [Google Scholar] [CrossRef]
- Zhao, T.; Hua, P.; Dai, W.; Zhang, J.; Ma, L. An optimal control method for discrete variable outdoor air volume setpoint determination in variable air volume systems. Build. Environ. 2020, 167, 106444. [Google Scholar] [CrossRef]
- Fritzson, P.; Engelson, V. Modelica—A unified object-oriented language for system modeling and simulation. In Proceedings of the European Conference on Object-Oriented Programming, Brussels, Belgium, 20–24 July 1998; pp. 67–90. [Google Scholar]
- Fritzson, P. Modelica—A cyber-physical modeling language and the OpenModelica environment. In Proceedings of the 7th International Wireless Communications and Mobile Computing Conference, Istanbul, Turkey, 4–8 July 2011; pp. 1648–1653. [Google Scholar]
- Felgner, F.; Merz, R.; Litz, L. Modular modelling of thermal building behavior using Modelica. Math. Comput. Model. Dyn. Syst. 2006, 12, 35–49. [Google Scholar] [CrossRef]
- Li, P.; Li, Y.; Seem, J.E.; Qiao, H.; Li, X.; Winkler, J. Recent advances in dynamic modeling of HVAC equipment. Part 2: Modelica-based modeling. HVAC R Res. 2014, 20, 150–161. [Google Scholar] [CrossRef]
- Wetter, M. A Modelica-Based Model Library for Building Energy and Control Systems; Lawrence Berkeley National Laboratory: Berkeley, CA, USA, July 2009.
- Wetter, M.; Zuo, W.; Nouidui, T.S.; Pang, X. Modelica buildings library. J. Build. Perform. Simul. 2014, 7, 253–270. [Google Scholar] [CrossRef]
- Wang, W.; Chen, J.; Hong, T.; Zhu, N. Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with Wi-Fi probe technology. Build. Environ. 2018, 138, 160–170. [Google Scholar] [CrossRef]
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Wang, W.; Shan, X.; Hussain, S.A.; Wang, C.; Ji, Y. Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO2 with OpenModelica Modeling. Energies 2020, 13, 4425. https://doi.org/10.3390/en13174425
Wang W, Shan X, Hussain SA, Wang C, Ji Y. Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO2 with OpenModelica Modeling. Energies. 2020; 13(17):4425. https://doi.org/10.3390/en13174425
Chicago/Turabian StyleWang, Wei, Xiaofang Shan, Syed Asad Hussain, Changshan Wang, and Ying Ji. 2020. "Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO2 with OpenModelica Modeling" Energies 13, no. 17: 4425. https://doi.org/10.3390/en13174425
APA StyleWang, W., Shan, X., Hussain, S. A., Wang, C., & Ji, Y. (2020). Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO2 with OpenModelica Modeling. Energies, 13(17), 4425. https://doi.org/10.3390/en13174425