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

Title:

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

Allard, Amélie (2013) A Linear Data-Driven System Identification Methodology for an Active/Passive Solar Thermal Storage System and Application to a Solar House. Masters thesis, Concordia University.

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Abstract

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.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Allard, Amélie
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:9 July 2013
Thesis Supervisor(s):Athienitis, Andreas K.
ID Code:977501
Deposited By: AMELIE ALLARD
Deposited On:18 Nov 2013 16:38
Last Modified:18 Jan 2018 17:44

References:

American Society of Heating, Refrigerating and Air-Conditioning Engineers. (2005). ASHRAE Handbook Fundamentals. Atlanta, GA.
Åström, K. J., & Eykhoff, P. (1971). System identification-A survey. Automatica, 7(2), 123–162.
Athienitis, A. K. (1997). Investigation of thermal performance of a passive solar building with floor radiant heating. Solar Energy, 61(5), 337–345.
Athienitis, A. K. (2007). Design of a Solar Home with BIPV-Thermal System and Ground-Source Heat Pump. Presented at the 2 nd Canadian Solar Buildings Conference, Calgary.
Athienitis, A. K., & Chen, Y. (2000). The effect of solar radiation on dynamic thermal performance of floor heating systems. Solar Energy, 69(3), 229–237.
Athienitis, A. K., & Santamouris, M. (2002). Thermal analysis and design of passive solar buildings (James & James.). London.
Athienitis, A. K., Stylianou, M., & Shou, J. (1990). A methodology for building thermal dynamics studies and control applications. ASHRAE Transactions, 96(2), 839–848.
Barton, P., Beggs, C. B., & Sleigh, P. A. (2002). A theoretical study of the thermal performance of the TermoDeck hollow core slab system. Applied Thermal Engineering, 22(13), 1485–1499.
Bekey, G. A. (1970). System identification- an introduction and a survey. Simulation, 15(4), 151–166.
Braun, J., & Chaturvedi, N. (2002). An Inverse Gray-Box Model for Transient Building Load Prediction. HVAC&R Research, 8(1), 73–99.
Candanedo, J. A. (2011). A Study of Predictive Control strategies for Optimally Designed Solar Homes (Ph.D. Thesis). Concordia University, Montréal.
Candanedo, J. A., Allard, A., & Athienitis, A. K. (2011). Predictive Control of Radiant Floor Heating and Transmitted Irradiance in a Room with High Solar Gains. ASHRAE Transactions, 117(2), 652–665.
Candanedo, J. A., & Athienitis, A. K. (2011). Predictive control of radiant floor heating and solar-source heat pump operation in a solar house. HVAC&R Research, 17(3), 235–256.
Candanedo, L. M. (2010). Modelling and Evaluation of the Performance of Building Integrated Open Loop Airbased Photovoltaic/Thermal Systems (Ph.D. Thesis). Concordia University.
Chae, Y. T., & Strand, R. K. (2013). Modeling ventilated slab systems using a hollow core slab: implementation in a whole building energy simulation program. Energy and Buildings, 57, 165–175.
Chen, T. Y. (2001). Real-time predictive supervisory operation of building thermal systems with thermal mass. Energy and Buildings, 33(2), 141–150.
Chen, T. Y. (2002). Application of adaptive predictive control to a floor heating system with a large thermal lag. Energy and Buildings, 34(1), 45–51.
Chen, T. Y., & Athienitis, A. K. (2003). Investigation of practical issues in building thermal parameter estimation. Building and Environment, 38(8), 1027–1038.
Chen, Y. (2009). Modeling and design of a solar house with focus on a ventilated concrete slab coupled with a building-integrated photovoltaic/thermal system (Master Thesis). Concordia University, Montreal.
Chen, Y., Athienitis, A. K., & Galal, K. (2010). 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. Solar Energy, 84(11), 1892–1907.
Corgnati, S. P., & Kindinis, A. (2007). Thermal mass activation by hollow core slab coupled with night ventilation to reduce summer cooling loads. Building and Environment, 42(9), 3285–3297.
Crawley, D. B., Hand, J. W., Kummert, M., & Griffith, B. T. (2008). Contrasting the capabilities of building energy performance simulation programs. Building and Environment, 43(4), 661–673.
Dewson, T., Day, B., & Irving, A. D. (1993). Least squares parameter estimation of a reduced order thermal model of an experimental building. Building and Environment, 28(2), 127–137.
Dodier, R. H., & Henze, G. P. (2004). Statistical Analysis of Neural Networks as Applied to Building Energy Prediction. Journal of Solar Energy Engineering, 126(1), 592.
Doiron, M. (2011). WholeBuilding Energy Analysis and Lessons Learned for a Near NetZero Energy Solar House (Master Thesis). Concordia University, Montreal.
Doiron, M., O’Brien, W., & Athienitis, A. K. (2011). Energy Performance, Comfort and Lessons Learned From a Near Net-Zero Energy Solar House. ASHRAE Transactions, 117(2), 585–596.
Duffie, J. A., & Beckman, W. A. (1980). Solar Engineering of Thermal Processes (Second Edition.). John Wiley & Sons, Inc.
Haberl, J. S., & Thamilseran, S. (1998). The Great Energy Predictor Shootout II - Measuring Retrofit Savings. ASHRAE Journal, 49–56.
Henze, G. P., Felsmann, C., Kalz, D. E., & Herkel, S. (2008). Primary energy and comfort performance of ventilation assisted thermo-active building systems in continental climates. Energy and Buildings, 40(2), 99–111.
Holcomb, D., Li, W., & Seshia, S. (2009). Algorithms for Green Buildings: Learning-Based Techniques for Energy Prediction and Fault Diagnosis (No. Technical Report No. UCB/EECS-2009-138). University of California at Berkeley.
Kalogirou, S. (2000). Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy, 25(5), 479–491.
Kalogirou, S. (2001). Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews, 5(4), 373–401.
Katipamula, S. (1996). Great Energy Predictor Shootout II: Modeling Energy Use in Large Commercial Buildings. ASHRAE Transactions, 102, 397–404.
Kumar, S., Sinha, S., Kojima, T., & Yoshida, H. (2001). Development of parameter based fault detection and diagnosis technique for energy efficient building management system. Energy Conversion and Management, 42(7), 833–854.
Kummert, M., André, P., & Argiriou, A. A. (2006). Comparing Control Strategies Using Experimental and Simulation Results: Methodology and Application to Heating Control of Passive Solar Buildings. HVAC&R Research, 12(3a), 715 – 737.
Lachal, B., Weber, W. U., & Guisan, O. (1992). Simplified methods for the thermal analysis of multifamily and administrative buildings, pp. 1151–1159. GA (USA).
Ljung, L. (2009). System identification: theory for the user (2nd ed.). Upper Saddle River, NJ: Prentice Hall PTR.
Ljung, Lennart. (2010). Perspectives on system identification. Annual Reviews in Control, 34(1), 1–12.
Monfet, D., Charneux, R., Zmeureanu, R., & Lemire, N. (2009). Calibration of a Building Energy Model Using Measured Data. ASHRAE Transactions, 115(1), 348–359.
Myhren, J., & Holmberg, S. (2008). Flow patterns and thermal comfort in a room with panel, floor and wall heating. Energy and Buildings, 40(4), 524–536.
O’Brien, W. (2011). Development of a Solar House Design Methodology and its Implementation into a Design Tool (Ph.D. Thesis). Concordia University, Montreal.
Olesen, B. W. (2002). Radiant Floor heating. In theory and Practice. ASHRAE Journal, 44(7), 19–26.
Peitsman, H. C., & Soethout, L. L. (1997). ARX models and real-time model-based diagnosis. ASHRAE Transactions, 103(1), 657–671.
Ren, M. J., & Wright, J. A. (1998). A ventilated slab thermal storage system model. Building and Environment, 33(1), 43–52.
Sattari, S., & Farhanieh, B. (2006). A parametric study on radiant floor heating system performance. Renewable Energy, 31(10), 1617–1626.
Wang, S., & Xu, . (2006a). Simplified building model for transient thermal performance estimation using GA-based parameter identification. International Journal of Thermal Sciences, 45, 419–432.
Wang, S., & Xu, X. (2006b). Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm. Energy Conversion and Management, 47(13-14), 1927–1941.
Weitzmann, P., Kragh, J., Roots, P., & Svendsen, S. (2005). Modelling floor heating systems using a validated two-dimensional ground-coupled numerical model. Building and Environment, 40(2), 153–163.
Wen, J., & Smith, T. F. (2003). Development and Validation of Online Parameter Estimation for HVAC Systems. Journal of Solar Energy Engineering, 125(3), 324.
Winwood, R., Benstead, R., Edwards, R., & Letherman, K. M. (1994). Building fabric thermal storage: Use of computational fluid dynamics for modelling. Building Services Engineering Research and Technology, 15(3), 171–178.
Yang, J., Rivard, H., & Zmeureanu, R. (2005). On-line building energy prediction using adaptive artificial neural networks. Energy and Buildings, 37(12), 1250–1259.
Zmeureanu, R., & Fazio, P. (1988). Thermal performance of a hollow core concrete floor system for passive cooling. Building and Environment, 23(3), 243–252.
Zouak, M., & Mechaqrane, A. (2004). A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building. Neural Computing & Applications, 13(1), 32–37.
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