Towards the real-life implementation of MPC for an office building: Identification issues

Modern control methods such as Model Predictive Control (MPC) are getting popular in recent years in many fields of industry. One of the branches that have witnessed great increase of interest in use of the MPC over the last few years is the building climate control area. According to the studies, the energy used in the building sector counts for 20–40% of the overall energy consumption. Almost half of this amount consists of heating, ventilation and air-conditioning (HVAC) costs which implies that energy consumption decrease in this area is one of the most interesting challenges today.

[1]  Frauke Oldewurtel,et al.  Use of partial least squares within the control relevant identification for buildings , 2013 .

[2]  S. Shah,et al.  Identification for long-range predictive control , 1991 .

[3]  J. A. Rossiter,et al.  Modelling and implicit modelling for predictive control , 2001 .

[4]  Gene F. Franklin,et al.  Feedback Control of Dynamic Systems , 1986 .

[5]  Yucai Zhu,et al.  Multivariable System Identification For Process Control , 2001 .

[6]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[7]  Clara Verhelst,et al.  Building models for model predictive control of office buildings with concrete core activation , 2013 .

[8]  Manfred Morari,et al.  Increasing energy efficiency in building climate control using weather forecasts and model predictive control , 2010 .

[9]  Francisco Rodríguez,et al.  A comparison of thermal comfort predictive control strategies , 2011 .

[10]  D. Laurí,et al.  PLS-based model predictive control relevant identification: PLS-PH algorithm , 2010 .

[11]  Sirish L. Shah,et al.  MPC relevant identification––tuning the noise model , 2004 .

[12]  Philip Haves,et al.  Model predictive control for the operation of building cooling systems , 2010, Proceedings of the 2010 American Control Conference.

[13]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[14]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[15]  Jiangfeng Zhang,et al.  Desiccant wheel thermal performance modeling for indoor humidity optimal control , 2013 .

[16]  E. Zacekova,et al.  Control relevant identification and predictive control of a building , 2012, 2012 24th Chinese Control and Decision Conference (CCDC).

[17]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[18]  S. Shah,et al.  A control-relevant identification strategy for GPC , 1992 .

[19]  S. García-Nieto,et al.  Model predictive control relevant identification: multiple input multiple output against multiple input single output , 2010 .

[20]  Steffen Petersen,et al.  The effect of weather forecast uncertainty on a predictive control concept for building systems operation , 2014 .

[21]  T. Y. Chen,et al.  Application of adaptive predictive control to a floor heating system with a large thermal lag , 2002 .

[22]  S. H Cho,et al.  Predictive control of intermittently operated radiant floor heating systems , 2003 .

[23]  Max Donath,et al.  American Control Conference , 1993 .

[24]  Samuel Prívara,et al.  Building modeling: Selection of the most appropriate model for predictive control , 2012 .

[25]  Nathan Mendes,et al.  Predictive controllers for thermal comfort optimization and energy savings , 2008 .

[26]  Biao Huang,et al.  Model predictive control relevant identification and validation , 2003 .

[27]  Yucai Zhu Multivariable process identification for mpc: the asymptotic method and its applications , 1998 .

[28]  Francesco Borrelli,et al.  Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Stimulation, and Experiments , 2012, IEEE Control Systems.

[29]  Manfred Morari,et al.  Importance of occupancy information for building climate control , 2013 .

[30]  L. Ljung Prediction error estimation methods , 2002 .