The paper suggests a simple energy saving controller for heating, ventilation, and air-conditioning (HVAC) systems that combines information on occupancy and weather with predictive control to save energy in buildings. The controller uses a pulse width modulation strategy and turns on/off the HVAC system based on the optimal decisions of the model predictive controller. The suggested controller is simple yet optimal (in a certain sense), and therefore suitable for residential and small commercial buildings where the cost of the controller is a key factor. The effectiveness of the proposed scheme is illustrated using simulations, whereas the model of the building thermal dynamics was identified based on data from experiments.
1. Afram, A. and Janabi-Sharifi, F. Review of modeling methods for HVAC systems. Appl. Therm. Eng., 2014, 67, 507–519.
https://doi.org/10.1016/j.applthermaleng.2014.03.055
2. Afram, A. and Janabi-Sharifi, F. Theory and applications of HVAC control systems – a review of model predictive control (MPC). Build. Environ., 2014, 72, 343–355.
https://doi.org/10.1016/j.buildenv.2013.11.016
3. Aswani, A., Master, N., Taneja, J., Culler, D., and Tomlin, C. Reducing transient and steady state electricity
consumption in HVAC using learning-based modelpredictive control. Proc. IEEE, 2012, 100, 240–253.
https://doi.org/10.1109/JPROC.2011.2161242
4. Bertsekas, D. P. Constrained Optimization and Lagrange Multiplier Methods. Athena Scientific, Belmont, MA, USA, 1996.
5. Burke, W. and Auslander, D. Low-frequency pulse width modulation design for HVAC compressors. In The ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 3. ASME, San Diego, CA, USA, 2009, 291–297.
https://doi.org/10.1115/DETC2009-87611
6. Dobbs, J. R. and Hencey, B. M. Model predictive HVAC control with online occupancy model. Energ. Buildings, 2014, 82, 675–684.
https://doi.org/10.1016/j.enbuild.2014.07.051
7. Dong, B. Integrated Building Heating, Cooling and Ventilation Control. PhD thesis, School of Architecture,
Carnegie Mellon University, 2010.
8. Drgoňa, J. and Kvasnica, M. Comparison of MPC strategies for building control. In The 19th International Conference on Process Control (Fikar, M. and Kvasnica, M., eds). IEEE, Štrbské Pleso, Slovakia, 2013, 401–406.
https://doi.org/10.1109/PC.2013.6581444
9. Erickson, V. L. and Cerpa, M. Á. C.-P. A. E. OBSERVE: Occupancy-based system for efficient reduction of
HVAC energy. In The 10th ACM/IEEE International Conference on Information Processing in Sensor Networks. IEEE, Chicago, IL, USA, 2011, 258–269.
10. Galanis, G. and Anadranistakis, M. A one-dimensional Kalman filter for the correction of near surface temperature forecasts. Meteorol. Appl., 2002, 9, 437–441.
https://doi.org/10.1017/S1350482702004061
11. Galanis, G., Louka, P., Katsafados, P., Pytharoulis, I., and Kallos, G. Applications of Kalman filters based on non-linear functions to numerical weather predictions. Ann. Geophys., 2006, 24, 2451–2460.
https://doi.org/10.5194/angeo-24-2451-2006
12. Goodwin, G. C., Graebe, S. F., and Salgado, M. E. Control System Design. Prentice Hall, Upper Saddle River, NJ, USA, 2001.
13. Gyalistras, D. and Gwerder, M. Use of weather and occupancy forecasts for optimal building climate control (OptiControl): two years progress report. Technical report, Terrestrial Systems Ecology ETH Zurich, Switzerland and Building Technologies Division, Siemens Switzerland Ltd., Zug, Switzerland, 2010. http://www.opticontrol.ethz.ch/Lit/Gyal 10 Opt iControl2YearsReport.pdf.
14. Haghighi, M. M. Modeling and Optimal Control Algorithm Design for HVAC Systems in Energy Efficient Buildings. Master’s thesis, University of California, 2011.
15. Lu, J., Sookoor, T., Srinivasan, V., Gao, G., Holben, B., Stankovic, J., et al. The smart thermostat: using occupancy sensors to save energy in homes. In The 8th ACM Conference on Embedded Networked Sensor Systems (Beutel, J., ed.). ACM, New York, NY, USA, Zurich, Switzerland, 2010, 211–224.
https://doi.org/10.1145/1869983.1870005
16. Ma, Y., Anderson, G., and Borrelli, F. A distributed predictive control approach to building temperature regulation. In The American Control Conference. IEEE, San Francisco, CA, USA, 2011, 2089–2094.
17. Ma, Y. and Borrelli, F. Fast stochastic predictive control for building temperature regulation. In The American Control Conference. IEEE, Montr´eal, Canada, 2012, 3075–3080.
18. Ma, Y., Borrelli, F., Hencey, B., Coffey, B., Bengea, S., and Haves, P. Model predictive control for the operation of building cooling systems. IEEE Trans. Control Syst. Technol., 2012, 20, 796–803.
https://doi.org/10.1109/TCST.2011.2124461
19. Ma, Y., Kelman, A., Daly, A., and Borrelli, F. Predictive control for energy efficient buildings with thermal storage: modeling, simulation, and experiments. IEEE Control Syst. Mag., 2012, 32(1), 44–64.
https://doi.org/10.1109/MCS.2011.2172532
20. Mallikarjun, S., Gautam, A. R., Muniyasamy, K., Maharaja, M., Subathra, B., and Srinivasan, S. LASSO based building thermal model for heating, ventilation and air-conditioning control. In The 1st IEEE International Conference on Electrical, Computer and Communication Technologies. IEEE, Coimbatore, India, 2015, 1–6.
https://doi.org/10.1109/ICECCT.2015.7226011
21. Morari, M., Gyalistras, D., and Schildbach, F.Weather forecasts enhance comfort and save energy. In Smart and Efficient Energy Council. Trento, Italy, 2009 (Talk).
22. Oldewurtel, F., Parisio, A., Jones, C. N., Gyalistras, D., Gwerder, M., Stauch, V., et al. Use of model predictive control and weather forecasts for energy efficient building climate control. Energ. Buildings, 2012, 45, 15–27.
https://doi.org/10.1016/j.enbuild.2011.09.022
23. Oldewurtel, F., Parisio, A., Jones, C. N., Morari, M., Gyalistras, D., Gwerder, M., et al. Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions. In The American Control Conference. IEEE, Baltimore, MD, USA, 2010, 5100–5105.
https://doi.org/10.1109/ACC.2010.5530680
24. Oldewurtel, F., Sturzenegger, D., and Morari, M. Importance of occupancy information for building climate control. Appl. Energy, 2013, 101, 521–532.
https://doi.org/10.1016/j.apenergy.2012.06.014
25. Oldewurtel, F., Ulbig, A., Parisio, A., Andersson, G., and Morari, M. Reducing peak electricity demand in building climate control using real-time pricing and model predictive control. In The 49th IEEE Conference on Decision and Control (Astolfi, A., ed.). IEEE, Atlanta, GA, USA, 2010, 1927–1932.
https://doi.org/10.1109/CDC.2010.5717458
26. Schildbach, F., Gyalistras, D., Gwerder, M., Jones, C. N., Parisio, A., Stauch, V., et al. Increasing energy efficiency in building climate control using weather forecasts and model predictive control. In Clima – RHEVA World Congress. Antalya, Turkey, 2010.
27. Široký, J., Oldewurtel, F., Cigler, J., and Prívara, S. Experimental analysis of model predictive control for an energy efficient building heating system. Appl. Energy, 2011, 88, 3079–3087.
https://doi.org/10.1016/j.apenergy.2011.03.009
28. Soleimani-Mohseni, M. Modelling and Intelligent Climate Control of Buildings. PhD thesis, Chalmers University of Technology, 2005.
29. Soudari, M., Srinivasan, S., Balasubramanian, S., Vain, J., and Kotta, Ü. Learning based personalized energy management systems for residential buildings. Energ. Buildings, 2016, 127, 953–968.
https://doi.org/10.1016/j.enbuild.2016.05.059
30. Spall, J. C. Estimation via Markov chain Monte Carlo. IEEE Control Syst. Mag., 2003, 23(2), 34–45.
https://doi.org/10.1109/MCS.2003.1188770
31. Thomas, B. and Soleimani-Mohseni, M. Intelligent thermostats save energy and give improved control performance. In The ACEEE Summer Study on Energy Efficiency in Buildings. ACEEE, Pacific Grove, CA, USA, 2002, 7.245–7.257.
32. Wei, X., Kusiak, A., Li, M., Tang, F., and Zeng, Y. Multiobjective optimization of the HVAC (heating, ventilation, and air conditioning) system performance. Energy, 2015, 83, 294–306.
https://doi.org/10.1016/j.energy.2015.02.024