Article | Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden | Learning Modelica Models from Component Libraries
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Learning Modelica Models from Component Libraries
Gregory Provan: Computer Science Department, University College Cork, Cork, Ireland Alex Feldman: PARC Inc., Palo Alto, CA 94304, USA
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Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden
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The Modelica language is one of the most important languages for representing a large class of systems, ranging from vehicles to climate control systems in buildings. Component libraries, containing components like valves, motors, pumps, etc., have been built to facilitate the construction of complex systems, but at present model construction and parameter estimation are entirely manual. We have developed software for (i) automating the process of constructing models that optimise a range of metrics (e.g., model-simulation accuracy or diagnostics accuracy), and (ii) dynamically updating the model parameters due to dynamic changes in the observed data and/or health status of the modelled system. We assume that in these component libraries a component may be modeled at multiple levels of fidelity, e.g., as a non-linear system (high-fidelity model), linear system, or a qualitative system (low-fidelity model). Choosing the right component model for system simulation is a difficult task and requires a search in the space of all possible component type combinations. In this paper we propose a method that automates this task and computes a system model that optimizes a set of metrics in a set of simulation scenarios. We describe initial experimental results showing the trade-offs of accuracy and inference time. This software has the potential to revolutionise how industry uses Modelica, i.e., changing the use from an expensive manual process to a fully automated process that is adaptive to changing external conditions.

Keywords: model composition; model fidelity and accuracy; simulation accuracy; model complexity

Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden

Gregory Provan, Alex Feldman
Learning Modelica Models from Component Libraries

Hirotugu Akaike. A new look at the statistical model identification. Automatic Control, IEEE Transactions on, 19(6):716–723, 1974.

Hirotugu Akaike. Likelihood of a model and information criteria. Journal of econometrics, 16(1):3–14, 1981.

Per Ă…kerlund. Model-based diagnosis using MathModelica. PhD thesis, Master’s thesis, Linköping University, SE-581 83 Linköping, 2001.

AC Antoulas, DC Sorensen, and S Gugercin. A survey of model reduction methods for large-scale systems. Contemporary mathematics, 280:193–220, 2001.

Olof Bäck. Modelling for diagnosis in Modelica: implementation and analysis. PhD thesis, Master’s thesis, Linköping University, SE-581 83 Link¨oping, 2008.

H BrĂĽckmann, J Strenkert, U Keller, B Wiesner, and A Junghanns. Model-based development of a dualclutch transmission using rapid prototyping and sil. In International VDI Congress Transmissions in Vehicles, 2009.

Francesco Casella and Alberto Leva. Modelica open library for power plant simulation: design and experimental validation. In Proceedings of the 3rd international Modelica conference, 2003.

Francesco Casella, Martin Otter, Katrin Proelss, Christoph Richter, and Hubertus Tummescheit. The modelica fluid and media library for modeling of incompressible and compressible thermo-fluid pipe networks. In Proceedings of the Modelica Conference, pages 631–640, 2006.

Francesco Casella, Filippo Donida, and Marco Lovera. Beyond simulation: Computer aided control system design using equation-based object oriented modelling for the next decade. In 2nd International Workshop on Equation-Based Object-Oriented Languages and Tools, page 35, 2008.

Johan de Kleer, Bill Janssen, Daniel G Bobrow, Tolga Kurtoglu, Bhaskar Saha, Nicholas R Moore, and Saravan Sutharshana. Fault augmented modelica models. In The 24th International Workshop on Principles of Diagnosis, pages 71–78, 2013.

Jing Du. The “weight” of models and complexity. Complexity, 2014.

S Elgsæter, Pal Kittilsen, and Svein Olav Hauger. Designing large-scale balanced-complexity models for online use. In Proc. IFAC Workshop on Automatic Control in Offshore Oil and Gas Production, Trondheim, Norway, pages 157–162, 2012.

Alexander Feldman, Gregory M Provan, and Arjan JC van Gemund. Computing observation vectors for maxfault min-cardinality diagnoses. In AAAI, pages 919–924, 2008.

Alexander Feldman, Helena Vicente de Castro, Arjan van Gemund, and Gregory Provan. Model-based diagnostic decision-support system for satellites. In Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, USA, pages 1–14, March 2013.

Lars Imsland, Pal Kittilsen, and Tor S Schei. Modelbased optimizing control and estimation using modelica model. Modeling, Identification and Control, 31(3): 107–121, 2010.

Elizabeth H Keating, John Doherty, Jasper A Vrugt, and Qinjun Kang. Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality. Water Resources Research, 46(10), 2010.

Eui-Jong Kim, Gilles Plessis, Jean-Luc Hubert, and Jean-Jacques Roux. Urban energy simulation: Simplification and reduction of building envelope models. Energy and Buildings, 84:193–202, 2014.

Pål Kittilsen, Svein Olav Hauger, and Stein OWasbø. Designing models for online use with modelica and fmi. In Proceedings of the 9th International Modelica Conference. Munich, Germany, pages 197–204, 2012.

Benjamin Kuipers and Karl Åström. The composition and validation of heterogeneous control laws. Automatica, 30(2):233–249, 1994.

Martin Kunz, Roberto Trotta, and David R Parkinson. Measuring the effective complexity of cosmological models. Physical Review D, 74(2):023503, 2006.

Marek Mateják, Tomaš Kulhánek, Jan Šilar, Pavol Privitzer, Filip Ježek, and Jirí Kofránek. Physiolibrarymodelica library for physiology. In 10th International Modelica Conference, pages 499–505. Link¨oping University Electronic Press Lund, Sweden, 2014.

S Pande, L Arkesteijn, HHG Savenije, and LA Bastidas. Hydrological model parameter dimensionality is a weak measure of prediction uncertainty. Natural Hazards and Earth System Sciences Discusions, 11, 2014, 2014.

Saket Pande, Mac McKee, and Luis A Bastidas. Complexity-based robust hydrologic prediction. Water resources research, 45(10), 2009.

Gilles Plessis, Aur´elie Kaemmerlen, and Amy Lindsay. Buildsyspro: a modelica library for modelling buildings and energy systems. In Proceedings of the International Modelica Conference, 2014.

Gregory Provan. Generating reduced-order diagnosis models for hvac systems. In Itnl. Workshop on Principles of Diagnosis, Murnau, Germany, 2011.

Gregory M Provan and JunWang. Automated benchmark model generators for model-based diagnostic inference. In IJCAI, pages 513–518, 2007.

Lucien A Schmit and B Farshi. Some approximation concepts for structural synthesis. AIAA journal, 12(5): 692–699, 1974.

G Schoups, NC Van de Giesen, and HHG Savenije. Model complexity control for hydrologic prediction. Water Resources Research, 44(12), 2008.

G. Schwarz. Estimating the dimension of a model. Ann. Statist., 6:461? U466, 1978.

Pol D Spanos. Linearization techniques for non-linear dynamical systems. PhD thesis, California Institute of Technology, 1977.

David J Spiegelhalter, Nicola G Best, Bradley P Carlin, and Angelika Van Der Linde. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4): 583–639, 2002.

Hubertus Tummescheit and Jonas Eborn. Design of a thermo-hydraulic model library in modelica. In 12th European Simulation Multiconference, 1998.

Jasper A Vrugt and Bruce A Robinson. Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and bayesian model averaging.
Water Resources Research, 43(1), 2007.

Eric-Jan Wagenmakers. A practical solution to the pervasive problems ofp values. Psychonomic bulletin& review, 14(5):779–804, 2007.

Michael Wetter, Wangda Zuo, Thierry S Nouidui, and Xiufeng Pang. Modelica buildings library. Journal of Building Performance Simulation, 7(4):253–270, 2014.

Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden

Gregory Provan, Alex Feldman
Learning Modelica Models from Component Libraries
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