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

Krzysztof Arendt, Muhyiddine Jradi, Michael Wetter, Christian Veje

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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.
Original languageEnglish
Title of host publicationProceedings of the 1st American Modelica Conference
EditorsMichael Tiller, Hubertus Tummescheit, Luigi Vanfretti
PublisherModelica Association and Linköping University Electronic Press
Publication dateOct 2018
ISBN (Electronic)9789176851487
Publication statusPublished - Oct 2018
EventThe American Modelica Conference 2018 - Cambridge, United States
Duration: 9. Oct 201810. Oct 2018


ConferenceThe American Modelica Conference 2018
Country/TerritoryUnited States
SeriesLinköping Electronic Conference Proceedings


  • FMI
  • parameter estimation
  • Python
  • open-source


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