A University Building Test Case for Occupancy-Based Building Automation
Abstract
:1. Introduction
2. Modelling of HVAC Dynamics
3. Optimization Problem Formulation
3.1. Optimization for Low-Level Controllers
3.2. Optimization for Set-Point Control
3.2.1. Thermal Comfort
3.2.2. Model Predictive Controller
4. Validation
5. Simulations
6. Conclusions and Future Work
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Nomenclature
Name | Description |
---|---|
T | Temperature (°C) |
Q | Input Power (kW) |
u | Control input |
c | Specific heat capacity (kJ/kg·K) |
Density (kg/m) | |
V | Volume (m) |
h | Heat transfer coefficient (W/mK) |
A | Area (m) |
q | Load due to external sources |
M | Metabolic rate (W/m) |
W | Rate of external work (=0 for office conditions) |
Subscripts | Description |
c | Chiller |
Cooling coil | |
Room | |
s | Supply air |
a | air |
w | water |
clothing | |
r | radiant surface |
References
- Pérez-Lombard, L.; Ortiz, J.; Pout, C. A review on buildings energy consumption information. Energy Build. 2008, 40, 394–398. [Google Scholar] [CrossRef]
- Knight, I. Assessing Electrical Energy Use in HVAC Systems. 2012. Available online: http://www.rehva.eu/fileadmin/hvac-dictio/01-2012/assessing-electrical-energy-use-in-hvac-systems_rj1201.pdf (accessed on 15 August 2018).
- Wemhoff, A. Calibration of HVAC equipment PID coefficients for energy conservation. Energy Build. 2012, 45, 60–66. [Google Scholar] [CrossRef]
- Schicktanz, M.; Nunez, T. Modelling of an adsorption chiller for dynamic system simulation. Int. J. Refrig. 2009, 32, 588–595. [Google Scholar] [CrossRef]
- Teitel, M.; Levi, A.; Zhao, Y.; Barak, M.; Bar-lev, E.; Shmuel, D. Energy saving in agricultural buildings through fan motor control by variable frequency drives. Energy Build. 2008, 40, 953–960. [Google Scholar] [CrossRef]
- Koh, J.; Zhai, J.Z.; Rivas, J.A. Comparative energy analysis of VRF and VAV systems under cooling mode. In Proceedings of the ASME 3rd International Conference on Energy Sustainability, ES2009, San Francisco, CA, USA, 19–23 July 2009; Volume 1, pp. 411–418. [Google Scholar]
- Henze, G.P.; Florita, A.R.; Brandemuehl, M.J.; Felsmann, C.; Cheng, H. Advances in near-optimal control of passive building thermal storage. J. Sol. Energy Eng. 2010, 132, 021009. [Google Scholar] [CrossRef]
- Wang, S.; Ma, Z. Supervisory and optimal control of building HVAC systems: A review. HVAC R Res. 2008, 14, 3–32. [Google Scholar] [CrossRef]
- Ma, Z.; Wang, S.; Xu, X.; Xiao, F. A supervisory control strategy for building cooling water systems for practical and real time applications. Energy Convers. Manag. 2008, 49, 2324–2336. [Google Scholar] [CrossRef]
- Nassif, N.; Moujaes, S. A cost-effective operating strategy to reduce energy consumption in a hvac system. Int. J. Energy Res. 2008, 32, 543–558. [Google Scholar] [CrossRef]
- Afram, A.; Janabi-Sharifi, F. Theory and applications of HVAC control systems—A review of model predictive control (MPC). Build. Environ. 2014, 72, 343–355. [Google Scholar] [CrossRef]
- Kontes, G.D.; Giannakis, G.I.; Kosmatopoulos, E.B.; Rovas, D.V. Adaptive-fine tuning of building energy management systems using co-simulation. In Proceedings of the 2012 IEEE International Conference on Control Applications, Dubrovnik, Croatia, 3–5 October 2012; pp. 1664–1669. [Google Scholar]
- Endel, P.; Holub, O.; Berka, J. Adaptive quantile estimation in performance monitoring of building automation systems. In Proceedings of the 2016 European Control Conference (ECC), Aalborg, Denmark, 29 June–1 July 2016; pp. 1189–1194. [Google Scholar]
- Giannakis, G.I.; Kontes, G.D.; Kosmatopoulos, E.B.; Rovas, D.V. A model-assisted adaptive controller fine-tuning methodology for efficient energy use in buildings. In Proceedings of the 2011 19th Mediterranean Conference on Control Automation (MED), Corfu, Greece, 20–23 June 2011; pp. 49–54. [Google Scholar]
- Brager, G.S.; De Dear, R. Climate, Comfort, & Natural Ventilation: A New Adaptive Comfort Standard for ASHRAE Standard 55; American Society of Heating, Refrigerating and air-Conditioning Engineers: Atlanta, GA, USA, 2001. [Google Scholar]
- ASHRAE. ANSI/ASHRAE Standard 55-2010: Thermal Environmental Conditions for Human Occupancy; American Society of Heating, Refrigerating and air-Conditioning Engineers: Atlanta, GA, USA, 2010. [Google Scholar]
- Mirakhorli, A.; Dong, B. Occupancy behavior based model predictive control for building indoor climate—A critical review. Energy Build. 2016, 129, 499–513. [Google Scholar] [CrossRef]
- Baldi, S.; Korkas, C.D.; Lv, M.; Kosmatopoulos, E.B. Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach. Appl. Energy 2018, 231, 1246–1258. [Google Scholar] [CrossRef]
- Oldewurtel, F.; Parisio, A.; Jones, C.N.; Gyalistras, D.; Gwerder, M.; Stauch, V.; Lehmann, B.; Morari, M. Use of model predictive control and weather forecasts for energy efficient building climate control. Energy Build. 2012, 45, 15–27. [Google Scholar] [CrossRef] [Green Version]
- Dong, B.; Lam, K.P. A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. In Building Simulation; Springer: Berlin, Germany, 2014; Volume 7, pp. 89–106. [Google Scholar]
- Pcolka, M.; Zacekova, E.; Robinett, R.; Celikovsky, S.; Sebek, M. Economical nonlinear model predictive control for building climate control. In Proceedings of the American Control Conference (ACC), Portland, OR, USA, 4–6 June 2014; pp. 418–423. [Google Scholar]
- Klaučo, M. Modeling of the Closed-Loop System with a Set of PID Controllers; Slovak University of Technology: Bratislava, Slovakia, 2016. [Google Scholar]
- Oldewurtel, F.; Sturzenegger, D.; Morari, M. Importance of occupancy information for building climate control. Appl. Energy 2013, 101, 521–532. [Google Scholar] [CrossRef]
- Sturzenegger, D.; Gyalistras, D.; Morari, M.; Smith, R.S. Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis. IEEE Trans. Control Syst. Technol. 2016, 24, 1–12. [Google Scholar] [CrossRef]
- Killian, M.; Kozek, M. Implementation of cooperative Fuzzy model predictive control for an energy-efficient office building. Energy Build. 2018, 158, 1404–1416. [Google Scholar] [CrossRef]
- EnergyPlus Official Website. Available online: https://energyplus.net (accessed on 21 June 2017).
- Baldi, S.; Michailidis, I.; Ravanis, C.; Kosmatopoulos, E.B. Model-based and model-free “plug-and-play” building energy efficient control. Appl. Energy 2015, 154, 829–841. [Google Scholar] [CrossRef]
- Baldi, S.; Karagevrekis, A.; Michailidis, I.T.; Kosmatopoulos, E.B. Joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids. Energy Convers. Manag. 2015, 101, 352–363. [Google Scholar] [CrossRef] [Green Version]
- Satyavada, H.; Baldi, S. An integrated control-oriented modelling for HVAC performance benchmarking. J. Build. Eng. 2016, 6, 262–273. [Google Scholar] [CrossRef] [Green Version]
- Wu, S.; Sun, J.Q. A physics-based linear parametric model of room temperature in office buildings. Build. Environ. 2012, 50, 1–9. [Google Scholar] [CrossRef]
- Baldi, S.; Yuan, S.; Endel, P.; Holub, O. Dual estimation: Constructing building energy models from data sampled at low rate. Appl. Energy 2016, 169, 81–92. [Google Scholar] [CrossRef]
- Maasoumy, M.; Sangiovanni-Vincentelli, A. Total and peak energy consumption minimization of building HVAC systems using model predictive control. IEEE Des. Test Comput. 2012, 29, 26–35. [Google Scholar] [CrossRef]
- Lauro, F.; Longobardi, L.; Panzieri, S. An adaptive distributed predictive control strategy for temperature regulation in a multizone office building. In Proceedings of the 2014 IEEE International Workshop on Intelligent Energy Systems (IWIES), San Diego, CA, USA, 5–8 October 2014; pp. 32–37. [Google Scholar]
- Fanger, P.O. Calculation of thermal comfort, Introduction of a basic comfort equation. ASHRAE Trans. 1967, 73, III–4. [Google Scholar]
- Michailidis, I.T.; Baldi, S.; Pichler, M.F.; Kosmatopoulos, E.B.; Santiago, J.R. Proactive control for solar energy exploitation: A german high-inertia building case study. Appl. Energy 2015, 155, 409–420. [Google Scholar] [CrossRef] [Green Version]
- Rohles, J.; Frederick, H. Thermal sensations of sedentary man in moderate temperatures. Hum. Fact. 1971, 13, 553–560. [Google Scholar] [CrossRef] [PubMed]
- Nguyen, T.A.; Aiello, M. Energy intelligent buildings based on user activity: A survey. Energy Build. 2013, 56, 244–257. [Google Scholar] [CrossRef] [Green Version]
VAV | VAV | VAV | VAV | |
---|---|---|---|---|
Gain | Room 1 | Room 2 | Room 3 | Corridor |
7.63 | 7.63 | 8.10 | 6.92 | |
0.66 | 0.66 | 0.70 | 0.65 |
Controller | Total Airflow (kg) | Consumption (kW) |
---|---|---|
Baseline PI | 1877.1 | 12.77 |
MPC + optimized PI | 1132.5 (−39.7%) | 7.70 (−39.7%) |
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Swaminathan, S.; Wang, X.; Zhou, B.; Baldi, S. A University Building Test Case for Occupancy-Based Building Automation. Energies 2018, 11, 3145. https://doi.org/10.3390/en11113145
Swaminathan S, Wang X, Zhou B, Baldi S. A University Building Test Case for Occupancy-Based Building Automation. Energies. 2018; 11(11):3145. https://doi.org/10.3390/en11113145
Chicago/Turabian StyleSwaminathan, Siva, Ximan Wang, Bingyu Zhou, and Simone Baldi. 2018. "A University Building Test Case for Occupancy-Based Building Automation" Energies 11, no. 11: 3145. https://doi.org/10.3390/en11113145