Identification of control-oriented thermal models of rooms in multi-room buildings

Model-based control for improving energy efficiency of buil dings has been a popular topic of late. Smart control requires a predictive mode l of the building’s thermal dynamics. Due to the complexity of the underlying physic al processes, usually system identification techniques are used to identify p arameters of a physicsbased grey-box model. We investigate questions of required mo el structure and identification techniques for parameter estimation of a sin gle zone model through a combination of analysis and experiments. Our results indi cate that a secondorder model can reproduce the input-output behavior of a ful l-scale model with 13 states. We also show that data collected during usual oper ation leads to poor parameter estimates that may nevertheless appear to predic t the temperature well. The error becomes apparent when there is sufficient differen c among various inputs and the output. We propose an algorithm to overcome th ese issues that involve specific forced-response tests. The results of this investigation are expected to provide guidelines on do’s and don’t’s in modeling a d identification of buildings for control.

[1]  Rp Rick Kramer,et al.  Simplified thermal and hygric building models： A literature review , 2012 .

[2]  James E. Braun,et al.  An Inverse Gray-Box Model for Transient Building Load Prediction , 2002 .

[3]  Paul M.J. Van den Hof,et al.  Determining Identifiable Parameterizations for Large-scale Physical Models in Reservoir Engineering , 2008 .

[4]  Yonggao Zhang,et al.  Control design and analysis of UPFC shunt converter , 2005, 2005 IEEE International Conference on Industrial Technology.

[5]  M. M. Gouda,et al.  Building thermal model reduction using nonlinear constrained optimization , 2002 .

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

[7]  Prabir Barooah,et al.  Zone-level control algorithms based on occupancy information for energy efficient buildings , 2012, 2012 American Control Conference (ACC).

[8]  Petru-Daniel Morosan,et al.  Building temperature regulation using a distributed model predictive control , 2010 .

[9]  Prabir Barooah,et al.  Issues in identification of control-oriented thermal models of zones in multi-zone buildings , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[10]  M. M. Gouda,et al.  Low-order model for the simulation of a building and its heating system , 2000 .

[11]  Henrik Madsen,et al.  Estimation of continuous-time models for the heat dynamics of a building , 1995 .

[12]  Gilles Fraisse,et al.  Development of a simplified and accurate building model based on electrical analogy , 2002 .

[13]  Prabir Barooah,et al.  A method for model-reduction of non-linear thermal dynamics of multi-zone buildings , 2012 .

[14]  Edward Henry Mathews,et al.  A first-order thermal model for building design , 1994 .

[15]  Henrik Madsen,et al.  Identifying suitable models for the heat dynamics of buildings , 2011 .

[16]  Jer-Nan Juang,et al.  Continuous-Time Bilinear System Identification , 2005 .

[17]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[18]  Manoranjan Majji,et al.  Continuous-time bilinear system identification using repeated experiments , 2010 .

[19]  Prabir Barooah,et al.  Occupancy-based zone-climate control for energy-efficient buildings: Complexity vs. performance , 2013 .

[20]  Bruce H. Krogh,et al.  Parameter identifiability for multi-zone building models , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[21]  Thomas Weng,et al.  Duty-cycling buildings aggressively: The next frontier in HVAC control , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.