ModestPy: An Open-Source Python Tool for Parameter Estimation in Functional Mock-up Units

Krzysztof Arendt
Center for Energy Informatics, University of Southern Denmark, Denmark

Muhyiddine Jradi
Center for Energy Informatics, University of Southern Denmark, Denmark

Michael Wetter
Lawrence Berkeley National Laboratory, USA

Christian T. Veje
Center for Energy Informatics, University of Southern Denmark, Denmark

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp18154121

Ingår i: Proceedings of The American Modelica Conference 2018, October 9-10, Somberg Conference Center, Cambridge MA, USA

Linköping Electronic Conference Proceedings 154:13, s. 121-130

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Publicerad: 2019-02-26

ISBN: 978-91-7685-148-7

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


The paper presents an open-source Python tool for parameter estimation in FMI-compliant models, called ModestPy. The tool enables estimation of model parameters using user-defined sequences of methods, which are particularly helpful in non-convex problems. A user can start estimation with a chosen global search method and subsequently refine the estimates with a local search method. Several methods are available already and the tool’s architecture allows for easily adding new ones. The advantages of having a single interface to multiple methods and using them in sequences are highlighted on a case study in which the parameters of a Modelica-based gray-box model of a building zone (nonlinear, multi-output) are estimated using 9 different combinations of methods. The methods are compared in terms of accuracy and computational performance


FMI, parameter estimation, Python, opensource


Johan Åkesson, Magnus Gäfvert, and Hubertus Tummescheit. JModelica—an open source platform for optimization of Modelica models. In Proceedings of MATHMOD 2009 - 6th Vienna International Conference on Mathematical Modelling, Vienna, Austria, February 2009. TU Wien.

Christian Andersson, Sofia Gedda, Johan Åkesson, and Stefan Diehl. Derivative-free parameter optimization of functional mock-up units. In Proceedings of the 9th International Modelica Conference - Munich, Germany, September 3, 2012. Modelica Association, 2012.

Christian Andersson, Johan Åkesson, and Claus Führer. PyFMI: A Python Package for Simulation of Coupled Dynamic Models with the Functional Mock-up Interface, volume LUTFNA-5008-2016 of Technical Report in Mathematical Sciences. Centre for Mathematical Sciences, Lund University, 2016.

Krzysztof Arendt. ModestPy: Parameter Estimation in FMIcompliant Models, 2017. URL https://github.com/ sdu-cfei/modest-py. [Online; accessed April 25, 2018].

Javier Bonilla, Jose Antonio Carballo, Lidia Roca, and Manuel Berenguel. Development of an open source multi-platform software tool for parameter estimation studies in fmi models. In Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017, number 132, pages 683–692. Linköping University Electronic Press, Linköpings universitet, 2017.

Marco Bonvini, MichaelWetter, and Michael D. Sohn. An FMIbased Framework for State and Parameter Estimation. In Modelica Conference 2014, 2014.

Richard H. Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu. A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput., 16(5):1190–1208, September 1995. ISSN 1064-8275. doi:10.1137/0916069.

Roel De Coninck and Lieve Helsen. Practical implementation and evaluation of model predictive control for an office building in brussels. Energy and Buildings, 111:290 – 298, 2016. ISSN 0378-7788. doi: https://doi.org/10.1016/j.enbuild.2015.11.014.

Roel De Coninck, Fredrik Magnusson, Johan Åkesson, and Lieve Helsen. Toolbox for development and validation of grey-box building models for forecasting and control. Journal of Building Performance Simulation, 9(3):288–303, 2016. doi:10.1080/19401493.2015.1046933.

Eric Jones, Travis Oliphant, Pearu Peterson, et al. SciPy: Open source scientific tools for Python, 2001. URL http://www.scipy.org/. [Online; accessed April 25, 2018].

Muhyiddine Jradi, Fisayo Caleb Sangogboye, Claudio Giovanni Mattera, Mikkel Baun Kjærgaard, Christian Veje, and Bo Nørregaard Jørgensen. A world class energy efficient university building by danish 2020 standards. Energy Procedia, 132:21 – 26, 2017. ISSN 1876-6102. doi:https://doi.org/10.1016/j.egypro.2017.09.625. 11th Nordic Symposium on Building Physics, NSB2017, 11-14 June 2017, Trondheim, Norway.

Rüdiger Kampfmann, Danny Mösch, and Nils Menager. Parameter Estimation based on FMI. In Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017, number 132, pages 313–319. Linköping University Electronic Press, Linköpings Universitet, 2017.

Dieter Kraft. A Software Package for Sequential Quadratic Programming. Deutsche Forschungs- und Versuchsanstalt für Luft- und Raumfahrt Köln: Forschungsbericht. Wiss. Berichtswesen d. DFVLR, 1988.

Stephen G. Nash. Newton-Type Minimization Via the Lanczos Method. SIAM Journal on Numerical Analysis, 21(4):770–788, 1984. ISSN 00361429.

Peter Rockett and Elizabeth Abigail Hathway. Model-predictive control for non-domestic buildings: a critical review and prospects. Building Research & Information, 45(5):556–571, 2017. doi:10.1080/09613218.2016.1139885.

Fisayo Caleb Sangogboye, Krzysztof Arendt, Ashok Singh, Christian T. Veje, Mikkel Baun Kjærgaard, and Bo Nørregaard Jørgensen. Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control. Building Simulation, 10(6):829–843, Dec 2017. ISSN 1996-8744. doi:10.1007/s12273-017-0397-5.

Luigi Vanfretti, Maxime Baudette, Achour Amazouz, Tetiana Bogodorova, Tin Rabuzin, Jan Lavenius, and Francisco José Goméz-López. Rapid: A modular and extensible toolbox for parameter estimation of modelica and fmi compliant models. SoftwareX, 5:144 – 149, 2016. ISSN 2352-7110. doi: https://doi.org/10.1016/j.softx.2016.07.004.

Michael Wetter. Genopt - a generic optimization program. In Roberto Lamberts, Cezar O. R. Negrão, and Jan Hensen, editors, Proc. of the 7th IBPSA Conference, volume I, pages 601–608, Rio de Janeiro, 2001. URL http://www.ibpsa.org/proceedings/BS2001/BS01_0601_608.pdf.

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