Article | Proceedings of the 11th International Modelica Conference, Versailles, France, September 21-23, 2015 | Methodology for Obtaining Linear State Space Building Energy Simulation Models
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Title:
Methodology for Obtaining Linear State Space Building Energy Simulation Models
Author:
Damien Picard: Mechanical engineering, KU Leuven, Belgium Filip Jorissen: Mechanical engineering, KU Leuven, Belgium / EnergyVille, Waterschei, Belgium Lieve Helsen: Mechanical engineering, KU Leuven, Belgium / EnergyVille, Waterschei, Belgium
DOI:
10.3384/ecp1511851
Download:
Full text (pdf)
Year:
2015
Conference:
Proceedings of the 11th International Modelica Conference, Versailles, France, September 21-23, 2015
Issue:
118
Article no.:
005
Pages:
51-58
No. of pages:
8
Publication type:
Abstract and Fulltext
Published:
2015-09-18
ISBN:
978-91-7685-955-1
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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Optimal climate control for building systems is facilitated by linear, low-order models of the building structure and of its Heating, Ventilation and Air Conditioning (HVAC) systems. However, obtaining these models in a practical form is often difficult, which greatly hampers the commercial implementation of optimal controllers. This work describes a methodology for obtaining a linear State Space Model (SSM) of Building Energy Simulation (BES) models, consisting of walls, windows, floors and the zone air. The methodology uses the Modelica library IDEAS to develop a BES model, including its non-linearities, and automates its linearisation. The Dymola function Linearize2 is used to generate the state space formulation, facilitating further mathematical manipulations, or simulation in different environments. Optionally this model can then be reduced for control purposes using model order reduction (MOR) techniques. The methodology is illustrated for an office building for which a maximum error of 0.7 K between the Modelica BES model and the SSM is observed.

Keywords: building energy simulation model; linearization; Dymola; model predictive control; model order reduction

Proceedings of the 11th International Modelica Conference, Versailles, France, September 21-23, 2015

Author:
Damien Picard, Filip Jorissen, Lieve Helsen
Title:
Methodology for Obtaining Linear State Space Building Energy Simulation Models
DOI:
http://dx.doi.org/10.3384/ecp1511851
References:

S.A. Klein et al. TRNSYS 17, A Transient System Simulation Program. Solar Energy Laboratory, University of Wisconsin, Madison, USA, http://sel.me.wisc.edu/trnsys, 2010.


H.B. Awbi and a. Hatton. Natural convection from heated room surfaces. Energy and Buildings, 30(3):233–244, August 1999.


R. Baetens, R. De Coninck, F. Jorissen, D. Picard, L. Helsen, and D. Saelens. OpenIDEAS - An open framework for integrated district energy simulations. In Submitted to Building simulation 2015, Hyderabad, 2015.


T. Defraeye, B. Blocken, and J. Carmeliet. Convective heat transfer coefficients for exterior building surfaces: Existing correlations and CFD modelling. Energy Conversion and Management, 52(1):512 – 522, 2011.


E. U. Finlayson, D. K. Arasteh, C. Huizenga, M. D. Rubin, and M. S. Reilly. Window 4.0: Documentation of calculation procedures. Technical report, 1993.


D. Gyalistras and M. Gwerder. Use of Weather and Occupancy Forecasts for optimal building climate control, two years progress report. Technical report, 2009.


M. Kummert. Contribution to the application of modern control techniques to solar buildings. Simulation-based approach and experimental validation. PhD thesis, Fondation Universitaire Luxembourgeoise, 2001.


B. Lehmann, D. Gyalistras, M. Gwerder, K.Wirth, and S. Carl. Intermediate complexity model for Model Predictive Control of Integrated Room Automation. Energy and Buildings, 58(0):250 – 262, 2013.


R.J. Liesen and C.O. Pedersen. An evaluation of inside surface heat balance models for cooling load calculations. Technical report, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta, GA (United States), 1997.


M. Otter. Modelica_linearsystems2, 2014. URL https://github.com/modelica/Modelica_LinearSystems2.git.


L. Perez-Lombard, J. Ortiz, and C. Pout. A review on buildings energy consumption information. Energy and Buildings, 40 (3):394–398, 2008.


M. Sourbron, C. Verhelst, and L. Helsen. Building models for model predictive control of office buildings with concrete core activation. Journal of Building Performance Simulation, 6(3):175–198, 2013.


R. Strand, F. Winkelmann, F. Buhl, J. Huang, R. Liesen, C. Pedersen, D. Fisher, R. Taylor, D. Crawley, and L. Lawrie. Enhancing and Extending the Capabilities of the Building Heat Balance Simulation Technique for use in EnergyPlus. In in Proceedings of Building Simulation’99, Volume II, pages 653–660, Kyoto, Japan, 1999.


D. Sturzenegger, D. Gyalistras, M. Morari, and Smith R. Semiautomated modular modeling of buildings for model predictive control. In BuildSys 2012 - Workshop of ACM SenSys Conference, Toronto, Canada, 2012.


D. Sturzenegger, D. Gyalistras, V. Semeraro, M. Morari, and R. Smith. BRCM Matlab Toolbox: Model Generation for Model Predictive Building Control. In American Control Conference, pages 1063–1069, Portland, June 2014.


C. Verhelst. Model Predictive Control of Ground Coupled Heat Pump Systems for Office Buildings. PhD thesis, KU Leuven, Belgium, 2012.

Proceedings of the 11th International Modelica Conference, Versailles, France, September 21-23, 2015

Author:
Damien Picard, Filip Jorissen, Lieve Helsen
Title:
Methodology for Obtaining Linear State Space Building Energy Simulation Models
DOI:
http://dx.doi.org/10.3384/ecp1511851
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