Thermal modeling of buildings with RC method and parameter estimation

Energy efficiency is becoming more important issue, especially in buildings sector where 15-20% of total energy in developed countries is used. One course toward higher efficiency is to improve HVAC (Heating, Ventilation and Air Conditioning) systems in building, especially by implementation of advanced control methods. Efficiency of these control methods (especially Model Predictive Control) significantly depends on quality of thermal models, where quality can be defined by accuracy and complexity. This paper investigates relationships between accuracy and complexity in thermal models produced with RC (Resistance-Capacitance) equivalent method, with parameters improved using optimization and estimation from data. Four experiments using simulation data regarding importance of selection of error function, selection of training data, selection of model complexity and reduction of complexity are conducted and explained.

[1]  Qi Luo,et al.  Building thermal network model and application to temperature regulation , 2010, 2010 IEEE International Conference on Control Applications.

[2]  H. Mirinejad,et al.  A review of intelligent control techniques in HVAC systems , 2012, 2012 IEEE Energytech.

[3]  Jagdev Singh,et al.  Fuzzy modeling and control of HVAC systems - A review , 2006 .

[4]  Kyoung-ho Lee,et al.  Reducing Peak Cooling Loads through Model-Based Control of Zone Temperature Setpoints , 2007, 2007 American Control Conference.

[5]  Bo Wahlberg,et al.  Physics-based modeling and identification for HVAC systems? , 2013, 2013 European Control Conference (ECC).

[6]  Ying Li,et al.  Predictive Control Model for Radiant Heating System Based on Neural Network , 2008, 2008 International Conference on Computer Science and Software Engineering.

[7]  Brandon Hencey,et al.  Online thermal estimation, control, and self-excitation of buildings , 2013, 52nd IEEE Conference on Decision and Control.

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

[9]  Hao Huang,et al.  Multi-zone temperature prediction in a commercial building using artificial neural network model , 2013, 2013 10th IEEE International Conference on Control and Automation (ICCA).

[10]  Prabir Barooah,et al.  A Method for model-reduction of nonlinear building thermal dynamics , 2011, Proceedings of the 2011 American Control Conference.

[11]  Andrew G. Alleyne,et al.  IDENTIFICATION OF BUILDING MODEL PARAMETERS AND LOADS USING ON-SITE DATA LOGS , 2008 .

[12]  J. M. Maestre,et al.  Distributed Model Predictive Control: An Overview and Roadmap of Future Research Opportunities , 2014, IEEE Control Systems.

[13]  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).

[14]  Mario Vasak,et al.  Parameter estimation for low-order models of complex buildings , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

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

[16]  Zeljko Hocenski,et al.  HVAC control methods - a review , 2015, 2015 19th International Conference on System Theory, Control and Computing (ICSTCC).

[17]  Manfred Morari,et al.  Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis , 2016, IEEE Transactions on Control Systems Technology.

[18]  Jirí Cigler,et al.  Building semi-physical modeling: On selection of the model complexity , 2013, 2013 European Control Conference (ECC).

[19]  MengChu Zhou,et al.  Model predictive control for HVAC systems — A review , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

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

[21]  Svend Svendsen,et al.  Modeling thermally active building components using space mapping , 2005 .

[22]  Prabir Barooah,et al.  Identification of multi-zone building thermal interaction model from data , 2011, IEEE Conference on Decision and Control and European Control Conference.

[23]  Sean P. Meyn,et al.  Building thermal model reduction via aggregation of states , 2010, Proceedings of the 2010 American Control Conference.

[24]  R. Sioshansi,et al.  Energy consumption of residential HVAC systems: A simple physically-based model , 2012, 2012 IEEE Power and Energy Society General Meeting.