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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)

Abstract

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

Nyckelord

FMI, parameter estimation, Python, opensource

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