A Linear Data-Driven System Identification Methodology for an Active/Passive Solar Thermal Storage System and Application to a Solar House

This thesis presents a methodology developed to identify a parametric model of a thermally-activated building system (TABS) using a system identification (SI) tool. The model was identified with collected data from an energy efficient solar single-family residential building, the Ecoterra™ house located in Eastman, Quebec. The TABS is a ventilated concrete slab (VCS) serving as an energy storage medium for active and passive solar gains in the basement. The system uses the structural mass of the house to store active solar gains collected by the building-integrated photovoltaic/thermal (BIPV/T) roof and passive solar gains entering the living space through the energy efficient windows. A data-driven system identification approach is used. Identifying a linear model and obtaining a low-order polynomial model were the main identification criteria. The thesis addresses the issues of the monitoring sensor accuracy on the model parameters, how physical knowledge of the VCS dynamic system can be considered during its validation and how the identification assumptions guide the future use of the model. This thesis also demonstrates that the identified linear polynomial model is an efficient tool to carry out redesign studies and possible control studies. Improved BIPV/T roof designs are compared based on increased solar energy utilization potential and potential increase of collected thermal energy stored into the VCS. The effect of modifying the BIPV/T roof angle and including a glazing section are analyzed and discussed, demonstrating the use of the identified transfer function model of the VCS as a more efficient and quicker alternative to whole building detailed simulations, particularly for comparing design and operation options on a relative basis.

[1]  W. Beckman,et al.  SOLAR ENGINEERING OF THERMAL PROCESSES Second Edition , 2009 .

[2]  Jeff Haberl,et al.  The great energy predictor shootout. II : Measuring retrofit savings , 1998 .

[3]  A. D. Irving,et al.  Least squares parameter estimation of a reduced order thermal model of an experimental building , 1993 .

[4]  Jesper Kragh,et al.  Modelling floor heating systems using a validated two-dimensional ground-coupled numerical model , 2005 .

[5]  Jonathan A. Wright,et al.  A ventilated slab thermal storage system model , 1998 .

[6]  T. Y. Chen,et al.  Real-time predictive supervisory operation of building thermal systems with thermal mass , 2001 .

[7]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[8]  Sanjay Kumar,et al.  Development of parameter based fault detection and diagnosis technique for energy efficient building management system , 2001 .

[9]  Shengwei Wang,et al.  Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm , 2006 .

[10]  Jin Wen,et al.  Development and Validation of Online Parameter Estimation for HVAC Systems , 2003 .

[11]  Bijan Farhanieh,et al.  A parametric study on radiant floor heating system performance , 2006 .

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

[13]  Wt O'Brien,et al.  Development of a Solar House Design Methodology and its Implementation into a Design Tool , 2011 .

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

[15]  Jin Yang,et al.  On-line building energy prediction using adaptive artificial neural networks , 2005 .

[16]  H. C. Peitsman,et al.  ARX models and real-time model-based diagnosis , 1997 .

[17]  Andreas K. Athienitis,et al.  Thermal Analysis and Design of Passive Solar Buildings , 2000 .

[18]  Karl Johan Åström,et al.  BOOK REVIEW SYSTEM IDENTIFICATION , 1994, Econometric Theory.

[19]  Gregor P. Henze,et al.  Statistical Analysis of Neural Networks as Applied to Building Energy Prediction , 2004 .

[20]  Stefano Paolo Corgnati,et al.  Thermal mass activation by hollow core slab coupled with night ventilation to reduce summer cooling loads , 2007 .

[21]  Mohcine Zouak,et al.  A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building , 2004, Neural Computing & Applications.

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

[23]  M. Stylianou,et al.  A methodology for building thermal dynamics studies and control applications , 1990 .

[24]  Andreas K. Athienitis,et al.  Predictive control of radiant floor heating and solar-source heat pump operation in a solar house , 2011 .

[25]  Lennart Ljung.  Perspectives on System Identification , 2008 .

[26]  R. Winwood,et al.  Building fabric thermal storage: Use of computational fluid dynamics for modelling , 1994 .

[27]  Sture Holmberg,et al.  Flow patterns and thermal comfort in a room with panel, floor and wall heating , 2008 .

[28]  Andreas K. Athienitis,et al.  The effect of solar radiation on dynamic thermal performance of floor heating systems , 2000 .

[29]  Clive B. Beggs,et al.  A theoretical study of the thermal performance of the TermoDeck hollow core slab system , 2002 .

[30]  Shengwei Wang,et al.  Simplified building model for transient thermal performance estimation using GA-based parameter identification , 2006 .

[31]  Candanedo Ibarra,et al.  A Study of Predictive Control Strategies for Optimally Designed Solar Homes , 2011 .

[32]  YuXiang Chen.  Modeling and design of a solar house with focus on a ventilated concrete slab coupled with a building-integrated photovoltaic/thermal system , 2009 .

[33]  Bernard Marie Lachal,et al.  Simplified methods for the thermal analysis of multifamily and administrative buildings , 1992 .

[34]  Paul Fazio,et al.  Thermal performance of a hollow core concrete floor system for passive cooling , 1988 .

[35]  George A. Bekey,et al.  System identification- an introduction and a survey , 1970 .

[36]  T. Y. Chen,et al.  Investigation of practical issues in building thermal parameter estimation , 2003 .

[37]  Bjarne W. Olesen,et al.  Radiant Floor Heating In Theory and Practice , 2002 .

[38]  Gregor P. Henze,et al.  Primary energy and comfort performance of ventilation assisted thermo-active building systems in continental climates , 2008 .

[39]  Michaël Kummert,et al.  Comparing Control Strategies Using Experimental and Simulation Results: Methodology and Application to Heating Control of Passive Solar Buildings , 2006 .

[40]  Andreas K. Athienitis,et al.  Investigation of thermal performance of a passive solar building with floor radiant heating , 1997 .

[41]  Matthew Anthony Doiron.  Whole-Building Energy Analysis and Lessons Learned for a Near Net-Zero Energy Solar House , 2011 .

[42]  Yuxiang Chen,et al.  Modeling, design and thermal performance of a BIPV/T system thermally coupled with a ventilated concrete slab in a low energy solar house: Part 1, BIPV/T system and house energy concept , 2010 .

[43]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

[44]  Young Tae Chae,et al.  Modeling ventilated slab systems using a hollow core slab: Implementation in a whole building energy simulation program , 2013 .

[45]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .