Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings
<p>Scheme of occupancy-based control.</p> "> Figure 2
<p>Overall scheme of baseline control (or RBC): (<b>a</b>) Overall scheme of baseline line control (or RBC) in summer cooling case; (<b>b</b>) Overall scheme of baseline line control (or RBC) in winter heating case.</p> "> Figure 3
<p>The proposed occupancy-based control setup for a building HVAC system.</p> "> Figure 4
<p>Overview of the occupancy prediction algorithms.</p> "> Figure 5
<p>Occupancy estimation using GSPS model: (<b>a</b>) Occupancy estimation using GSPS model 3000 points; (<b>b</b>) Occupancy estimation using GSPS model 5000 points.</p> "> Figure 6
<p>Occupancy estimation using EM algorithm.</p> "> Figure 7
<p>Occupancy estimation using three different basis functions: (<b>a</b>) Gaussian basis functions; (<b>b</b>) Laplace basis functions; (<b>c</b>) Uniform basis functions.</p> "> Figure 8
<p>Temperature set point for uncertain basis method.</p> ">
Abstract
:1. Introduction
1.1. Background of Research
1.2. Literature Review
1.2.1. Occupancy Models
1.2.2. Occupancy-Based Control
1.3. Main Idea and Outline
2. Problem Formulation
2.1. Building Thermal Model
2.2. Baseline Control Strategy (or RBC)
2.3. System Model
- StatesInputs
- Disturbance
2.4. Cost Function
2.5. Temperature Set Algorithm
Algorithm 1 Temperature Setting Algorithm [34] |
|
3. Occupancy Prediction Algorithms
3.1. Expectation Maximization (EM)
3.2. Finite State Automata (FSA)
3.3. Simplified Binary States FSA
3.4. Estimating Number of Occupants
FSA with 3 or More Input/Output Values
3.5. Basis Function
4. Case Studies
4.1. Definition of the Performance Indexes
4.2. Occupancy Prediction Performance
4.2.1. GSPS Model
4.2.2. EM Method
4.2.3. Uncertain Basis Functions
4.3. Temperature Set Points
4.4. Occupancy-Based Control
4.5. Summary of the Results
5. Conclusions and Future Work
Author Contributions
Acknowledgments
Conflicts of Interest
References
- Erickson, V.L.; Carreira-Perpiñán, M.Á.; Cerpa, A.E. Occupancy modeling and prediction for building energy management. ACM Trans. Sens. Net. 2014, 10, 42. [Google Scholar] [CrossRef]
- Sun, B.; Luh, P.B.; Jia, Q.S.; Jiang, Z.; Wang, F.; Song, C. Building energy management: Integrated control of active and passive heating, cooling, lighting, shading, and ventilation systems. IEEE Trans. Automation Sci. Eng. 2013, 10, 588–602. [Google Scholar]
- Tzempelikos, A.; Athienitis, A.K. The impact of shading design and control on building cooling and lighting demand. Solar Energy 2007, 81, 369–382. [Google Scholar] [CrossRef]
- Van Moeseke, G.; Bruyère, I.; De Herde, A. Impact of control rules on the efficiency of shading devices and free cooling for office buildings. Build. Environ. 2007, 42, 784–793. [Google Scholar] [CrossRef]
- Valdiserri, P.; Biserni, C.; Garai, M. Energy performance of a ventilation system for an apartment according to the Italian regulation. Inter. J. Energy Environ. Engin. 2016, 7, 353–359. [Google Scholar] [CrossRef]
- Erickson, V.L.; Carreira-Perpiñán, M.Á.; Cerpa, A.E. OBSERVE: Occupancy-based system for efficient reduction of HVAC energy. In Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, Chicago, IL, USA, 12–14 April 2011; pp. 258–269. [Google Scholar]
- Shih, H.C. A robust occupancy detection and tracking algorithm for the automatic monitoring and commissioning of a building. Energy Build. 2014, 77, 270–280. [Google Scholar] [CrossRef]
- Sharma, I.; Dong, J.; Malikopoulos, A.A.; Street, M.; Ostrowski, J.; Kuruganti, T.; Jackson, R. A modeling framework for optimal energy management of a residential building. Energy Build. 2016, 130, 55–63. [Google Scholar] [CrossRef]
- Dong, J.; Olama, M.M.; Kuruganti, T.; Nutaro, J.; Xue, Y.; Sharma, I.; Djouadi, S.M. Adaptive building load control to enable high penetration of solar photovoltaic generation. In Proceedings of the Power & Energy Society General Meeting IEEE, Chicago, IL, USA, 16–20 July 2017; pp. 1–5. [Google Scholar]
- Rafsanjani, H.N.; Ahn, C.R.; Alahmad, M. A review of approaches for sensing, understanding, and improving occupancy-related energy-use behaviors in commercial buildings. Energies 2015, 8, 10996–11029. [Google Scholar] [CrossRef]
- Brandemuehl, M.J.; Braun, J.E. The impact of demand-controlled and economizer ventilation strategies on energy use in buildings. ASHRAE Trans. 1999, 105, 39. [Google Scholar]
- Harle, R.K.; Hopper, A. The potential for location-aware power management. In Proceedings of the 10th international conference on Ubiquitous computing, Seoul, Korea, 21–24 September 2008; pp. 302–311. [Google Scholar]
- Erickson, V.L.; Cerpa, A.E. Occupancy based demand response HVAC control strategy. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland, 3–5 November 2010; pp. 7–12. [Google Scholar]
- Garg, V.; Bansal, N. Smart occupancy sensors to reduce energy consumption. Energy Build. 2000, 32, 81–87. [Google Scholar] [CrossRef]
- 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]
- Labeodan, T.; Zeiler, W.; Boxem, G.; Zhao, Y. Occupancy measurement in commercial office buildings for demand-driven control applications—A survey and detection system evaluation. Energy Build. 2015, 93, 303–314. [Google Scholar] [CrossRef]
- Gunay, H.B.; O’Brien, W.; Beausoleil-Morrison, I. A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices. Build. Environ. 2013, 70, 31–47. [Google Scholar] [CrossRef]
- Parys, W.; Saelens, D.; Hens, H. Coupling of dynamic building simulation with stochastic modelling of occupant behaviour in offices–a review-based integrated methodology. J. Build. Perform. Simul. 2011, 4, 339–358. [Google Scholar] [CrossRef]
- Gunay, H.B.; O’Brien, W.; Beausoleil-Morrison, I. Implementation and comparison of existing occupant behaviour models in EnergyPlus. J. Build. Perform. Simul. 2016, 9, 567–588. [Google Scholar] [CrossRef]
- Yang, J.; Santamouris, M.; Lee, S.E. Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings. Energy Build. 2016, 121, 344–349. [Google Scholar] [CrossRef]
- Louis, J.N.; Caló, A.; Leiviskä, K.; Pongrácz, E. Modelling home electricity management for sustainability: The impact of response levels, technological deployment & occupancy. Energy Build. 2016, 119, 218–232. [Google Scholar] [Green Version]
- Peng, Y.; Rysanek, A.; Nagy, Z.; Schlüter, A. Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Appl. Energy 2018, 211, 1343–1358. [Google Scholar] [CrossRef]
- Chaney, J.; Owens, E.H.; Peacock, A.D. An evidence based approach to determining residential occupancy and its role in demand response management. Energy Build. 2016, 125, 254–266. [Google Scholar] [CrossRef]
- Wang, W.; Chen, J.; Huang, G.; Lu, Y. Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution. Appl. Energy 2017, 207, 305–323. [Google Scholar] [CrossRef]
- D’Oca, S.; Hong, T. Occupancy schedules learning process through a data mining framework. Energy Build. 2015, 88, 395–408. [Google Scholar] [CrossRef] [Green Version]
- Scott, J.; Bernheim Brush, A.; Krumm, J.; Meyers, B.; Hazas, M.; Hodges, S.; Villar, N. PreHeat: controlling home heating using occupancy prediction. In Proceedings of the 13th international conference on Ubiquitous computing, Beijing, China, 17–21 September 2011; pp. 281–290. [Google Scholar]
- Lu, J.; Sookoor, T.; Srinivasan, V.; Gao, G.; Holben, B.; Stankovic, J.; Field, E.; Whitehouse, K. The smart thermostat: using occupancy sensors to save energy in homes. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, Zurich, Switzerland, 3–5 November 2010; pp. 211–224. [Google Scholar]
- Gunay, H.B.; O’Brien, W.; Beausoleil-Morrison, I. Development of an occupancy learning algorithm for terminal heating and cooling units. Build. Environ. 2015, 93, 71–85. [Google Scholar] [CrossRef]
- Peng, Y.; Rysanek, A.; Nagy, Z.; Schlüter, A. Occupancy learning-based demand-driven cooling control for office spaces. Build. Environ. 2017, 122, 145–160. [Google Scholar] [CrossRef]
- Karjalainen, S. Should we design buildings that are less sensitive to occupant behaviour? A simulation study of effects of behaviour and design on office energy consumption. Energy Efficiency 2016, 9, 1257–1270. [Google Scholar] [CrossRef]
- Oldewurtel, F.; Sturzenegger, D.; Morari, M. Importance of occupancy information for building climate control. Appl. Energy 2013, 101, 521–532. [Google Scholar] [CrossRef]
- Oldewurtel, F.; Parisio, A.; Jones, C.N.; Morari, M.; Gyalistras, D.; Gwerder, M.; Stauch, V.; Lehmann, B.; Wirth, K. Energy efficient building climate control using stochastic model predictive control and weather predictions. In Proceedings of the 2010 American Control Conference, Baltimore, MD, USA, 30 June–2 July 2010; pp. 5100–5105. [Google Scholar]
- 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, J.; Winstead, C.; Djouadi, S.M.; Nutaro, J.J.; Kuruganti, T. Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings. In Proceedings of the 4th International High Performance Buildings Conference at Purdue, West Lafayette, IN, USA, 11–14 July 2016. [Google Scholar]
- Gaetani, I.; Hoes, P.J.; Hensen, J.L. Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy. Energy Build. 2016, 121, 188–204. [Google Scholar] [CrossRef]
- Gwerder, M.; Tödtli, J. Predictive control for integrated room automation. In Proceedings of the 8th REHVA World Congress Clima, Lausanne, Switzerland, 9–12 October 2005. [Google Scholar]
- Olama, M.M.; Kuruganti, T.; Nutaro, J.J.; Dong, J. Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid. Energies 2018, 11, 1852. [Google Scholar] [CrossRef]
- Avci, M.; Erkoc, M.; Asfour, S.S. Residential HVAC load control strategy in real-time electricity pricing environment. In Proceedings of the 2012 IEEE Energytech, Cleveland, OH, USA, 29–31 May 2012; pp. 1–6. [Google Scholar]
- Dong, J.; Ma, X.; Djouadi, S.; Li, H.; Liu, Y. Frequency Prediction of Power Systems in FNET Based on State-Space Approach and Uncertain Basis Functions. IEEE Trans. Power Syst. 2014, 29, 2602–2612. [Google Scholar] [CrossRef]
- Verhaegen, M.; Verdult, V. Filtering and System identification: a Least Squares Approach; Cambridge University Press: Cambridgeshire, UK, 2007. [Google Scholar]
- Paz, A. Introduction to Probabilistic Automata; Academic Press: Manhattan, NY, USA, 2014. [Google Scholar]
- Lyngso, R.B.; Pedersen, C.; Nielsen, H. Metrics and similarity measures for hidden Markov models. In Proceedings of the 7th International Conference on Intelligent Systems for Molecular Biology, Heidelberg, Germany, 6–10 August 1999; pp. 178–186. [Google Scholar]
- Mohri, M. Finite-state transducers in language and speech processing. Comput. Linguist. 1997, 23, 269–311. [Google Scholar]
- Klir, G. An Approach to General Systems Theory; Van Nostrand Reinhold: New York, NY, USA, 1969. [Google Scholar]
- Klir, G. Architecture of Systems Problem Solving; Springer Science & Business Media: Berlin, Germany, 2013. [Google Scholar]
- Dong, J.; Ma, X.; Djouadi, S.M.; Li, H.; Liu, Y. Frequency prediction of power systems in FNET based on state-space approach and uncertain basis functions. IEEE Trans. Power Syst. 2014, 29, 2602–2612. [Google Scholar] [CrossRef]
- Dong, J.; Kuruganti, T.; Djouadi, S.M. Very short-term photovoltaic power forecasting using uncertain basis function. In Proceedings of the Information Sciences and Systems (CISS) 51st Annual Conference on IEEE, Baltimore, MD, USA, 22–24 March 2017; pp. 1–6. [Google Scholar]
- Kay, S. Signal Fitting With Uncertain Basis Functions. Signal Process. Lett. IEEE 2011, 18, 383–386. [Google Scholar] [CrossRef]
Variables | Definition |
---|---|
Indoor air temperature (C) | |
Interior-wall temperature (C) | |
Exterior-wall core temperature (C) | |
Cooling power () (kW) | |
Heating power () (kW) | |
Ambient temperature (C) | |
Solar radiation (kW/m2) | |
Internal heat gain (kW) |
Building Parameter Values | Unit |
---|---|
C1 = 9.356 ×10 5 | kJ/C |
C2 = 2.970 × 10 6 | kJ/C |
Cw = 6.695 × 10 5 | kJ/C |
K1 = 16.48 | kJ/C |
K2 = 108.5 | kJ/C |
K3 = 5 | kJ/C |
K4 = 30.5 | kJ/C |
K5 = 23.04 | kJ/C |
Round | Time () | Occupancy () |
---|---|---|
11 | ||
10 | ⓑ | |
9 | ⓐ | |
8 | ⓑ | |
7 | a | |
6 | a | |
5 | b | |
4 | a | |
3 | b | |
2 | b | |
1 | a |
Input | Output | Count | Likelihood |
---|---|---|---|
aaa | a | 47 | 0.959 |
b | 2 | 0.041 | |
aab | a | 0 | 0 |
b | 1 | 1 | |
aba | a | 1 | 1 |
b | 0 | 0 | |
abb | a | 0 | 0 |
b | 1 | 1 | |
baa | a | 1 | 0.5 |
b | 1 | 0.5 | |
bab | a | 1 | 0.33 |
b | 2 | 0.67 | |
bba | a | 0 | 0 |
b | 1 | 1 | |
bbb | a | 0 | 0 |
b | 4 | 1 |
Methods | Estimation RMSE | Accuracy |
---|---|---|
GSPS (3000) | 3.078 | 70.0% |
GSPS (5000) | 2.646 | 71.5% |
EM | 3.715 | 61.5% |
Basis-Gaussian | 3.211 | 68.4% |
Basis-Laplace | 2.946 | 70.9% |
Basis-Uniform | 2.571 | 72.6% |
Methods | Control Cost () | Energy Saving |
---|---|---|
Basic Control (No Occupancy info) | 5.43 | 0% |
Basis-Gaussian (Basic Control) | 4.77 | 13% |
Basis-Gaussian (MPC) | 4.38 | 20% |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
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. https://doi.org/10.3390/en11092427
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(9):2427. https://doi.org/10.3390/en11092427
Chicago/Turabian StyleDong, Jin, Christopher Winstead, James Nutaro, and Teja Kuruganti. 2018. "Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings" Energies 11, no. 9: 2427. https://doi.org/10.3390/en11092427