Simulation-based Short-term Model Predictive Control for HVAC Systems of Residential Houses

In this paper, we propose a simple model predictive control (MPC) scheme for Heating,ventilation, and air conditioning (HVAC) systems in residential houses. Our control scheme utilizes afitted thermal simulation model for each house to achieve precise prediction of room temperature andenergy consumption in each prediction period. The set points for each control step of HVAC systemsare selected to minimize the amount of energy consumption while maintaining room temperaturewithin a desirable range to satisfy user comfort. Our control system is simple enough to implement inresidential houses and is more ecient comparing with rule-based control methods.Keywords Model predictive control, air conditioning, thermal simulation References 1] R. Rajkumar, I.L.I. Lee, L.S.L. Sha, J. Stankovic, Cyber-physical systems: The next computingrevolution, in: Proceedings of the 47th Design Automation Conference. (2010) 731–736. https://doi.org/10.1145/1837274.1837461.[2] M. Schmidt, C. Ahlund, Smart buildings as Cyber-Physical Systems: Data-driven predictivecontrol strategies for energy eciency, Renewable and Sustainable Energy Reviews. 90 (2018) 742–756.https://doi.org/10.1016/j.rser.2018.04.013.[3] F. Oldewurtel, A. Parisio, C.N. Jones, D. Gyalistras, M. Gwerder, V. Stauch, B. Lehmann, M. Morari, Useof model predictive control and weather forecasts for energy ecient building climate control, Energy andBuildings. 45 (2012) 15–27. https://doi.org/10.1016/j.enbuild.2011.09.022.[4] J. Hu, P. Karava, Model predictive control strategies for buildings with mixed-mode cooling, Building andEnvironment. 71 (2014) 233–244. https://doi.org/10.1016/j.buildenv.2013.09.005.[5] Y. Kwak, J.H. Huh, C. Jang, Development of a model predictive control framework through real-timebuilding energy management system data, Applied Energy. 155 (2015) 1–13. https://doi.org/10.1016/j.apenergy.2015.05.096.[6] A. Afram, F. Janabi-Sharifi, Supervisory model predictive controller (MPC) for residential HVACsystems: Implementation and experimentation on archetype sustainable house in Toronto, Energy andBuildings. 154 (2017) 268–282. https://doi.org/10.1016/j.enbuild.2017.08.060.[7] H. Nguyen, Y. Makino, A.O. Lim, Y. Tan, Y. Shinoda, Building high-accuracy thermal simulationfor evaluation of thermal comfort in real houses, in: Lecture Notes in Computer Science, Vol. 7910 LNCS,Springer, Berlin, Heidelberg. 2013, pp. 159–166. https://doi.org/10.1007/978-3-642-39470-6-20.[8] R. De Coninck, L. Helsen, Practical implementation and evaluation of model predictive control for an ocebuilding in Brussels, Energy and Buildings. 111 (2016) 290–298. https://doi.org/10.1016/j.enbuild.2015.11.014.[9] D. Sturzenegger, D. Gyalistras, M. Morari, R.S. Smith, Model Predictive Climate Control of a Swiss OceBuilding: Implementation, Results, and Cost-Benefit Analysis, IEEE Transactions on Control SystemsTechnology. 24(1) (2016) 1–12. https://doi.org/10.1109/TCST.2015.2415411.[10] F. Ascione, N. Bianco, C. De Stasio, G.M. Mauro, G.P. Vanoli, Simulation-based model predictive controlby the multi-objective optimization of building energy performance and thermal comfort, Energy andBuildings. 111 (2016) 131–144. https://doi.org/10.1016/j.enbuild.2015.11.033.[11] J. Siroký, F. Oldewurtel, J. Cigler, S. Privara, Experimental analysis of model predictive control foran energy ecient building heating system, Applied Energy. 88(9) (2011) 3079–3087. https://doi.org/10.1016/j.apenergy.2011.03.009.[12] James J. Hirsch, DOE-2 Building Energy Use and Cost Analysis Tool (2013). http://doe2.com/DOE2/index.html (accessed 24 October 2018)[13] US Department of Energy, Energy Eciency and Renewable Energy Oce, Building Technology Program, EnergyPlus 8.9.0 (2018). https://energyplus.net (accessed 24 October 2018)[14] Solar Energy Laboratory, TRNSYS 18: A Transient System Simulation Program, University of Wisconsin, Madison. http://sel.me.wisc.edu/trnsys (accessed 24 October 2018)[15] M. Wallace, R. McBride, S. Aumi, P. Mhaskar, J. House, T. Salsbury, Energy ecient model predictive building temperature control, Chemical Engineering Science. 69(1) (2012) 45–58. https://doi.org/10.1016/j.ces.2011.07.023.[16] O. Sian En, M. Yoshiki, Y. Lim, Y. Tan, Predictive thermal comfort control for cyber-physical home systems, in: Proceedings of 2018 13th Annual Conference on System of Systems Engineering (SoSE).(2018) 444–451. https://doi.org/10.1109/SYSOSE.2018.8428734.[17] A.I. Dounis, C.Caraiscos, Advanced control systems engineering for energy and comfort management in a building environment—a review, Renewable and Sustainable Energy Reviews. 13(6-7) (2009) 1246–1261. https://doi.org/10.1016/j.rser.2008.09.015.[18] P. Hoppe, The physiological equivalent temperature - A universal index for the biometeorological assessment of the thermal environment, International Journal of Biometeorology. 43(2) (1999) 71–75. https://doi.org/10.1007/s004840050118.