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

Krzysztof  Arendt; Muhyiddine Jradi; Michael Wetter; Christian  Veje

doi:10.3384/ecp18154121

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

Krzysztof Arendt
, Muhyiddine Jradi
, Michael Wetter
, Christian Veje

Lawrence Berkeley National Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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.

Original language	English
Title of host publication	Proceedings of the 1st American Modelica Conference
Editors	Michael Tiller, Hubertus Tummescheit, Luigi Vanfretti
Publisher	Modelica Association and Linköping University Electronic Press
Publication date	Oct 2018
Pages	121-130
ISBN (Electronic)	9789176851487
DOIs	https://doi.org/10.3384/ecp18154121
Publication status	Published - Oct 2018
Event	The American Modelica Conference 2018 - Cambridge, United States Duration: 9. Oct 2018 → 10. Oct 2018

Conference

Conference	The American Modelica Conference 2018
Country/Territory	United States
City	Cambridge
Period	09/10/2018 → 10/10/2018

Series	Linköping Electronic Conference Proceedings
Volume	154
ISSN	1650-3686

Keywords

FMI
parameter estimation
Python
open-source

Documents & Links

10.3384/ecp18154121

Preprint versionAccepted manuscript, 981 KBLicence: Unspecified
166522_Modelica_Proceedings_v2Final published version, 31.1 MBLicence: CC BY-NC

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Arendt, Krzysztof ; Jradi, Muhyiddine ; Wetter, Michael et al. / ModestPy : An Open-Source Python Tool for Parameter Estimation in Functional Mock-up Units. Proceedings of the 1st American Modelica Conference. editor / Michael Tiller ; Hubertus Tummescheit ; Luigi Vanfretti. Modelica Association and Linköping University Electronic Press, 2018. pp. 121-130 (Linköping Electronic Conference Proceedings, Vol. 154).

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

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{\textquoteright}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.",

keywords = "FMI, parameter estimation, Python, open-source",

author = "Krzysztof Arendt and Muhyiddine Jradi and Michael Wetter and Christian Veje",

year = "2018",

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doi = "10.3384/ecp18154121",

language = "English",

series = "Link{\"o}ping Electronic Conference Proceedings",

publisher = "Modelica Association and Link{\"o}ping University Electronic Press",

pages = "121--130",

editor = "Michael Tiller and Hubertus Tummescheit and Luigi Vanfretti",

booktitle = "Proceedings of the 1st American Modelica Conference",

note = "The American Modelica Conference 2018 ; Conference date: 09-10-2018 Through 10-10-2018",

}

Arendt, K, Jradi, M, Wetter, M & Veje, C 2018, ModestPy: An Open-Source Python Tool for Parameter Estimation in Functional Mock-up Units. in M Tiller, H Tummescheit & L Vanfretti (eds), Proceedings of the 1st American Modelica Conference. Modelica Association and Linköping University Electronic Press, Linköping Electronic Conference Proceedings, vol. 154, pp. 121-130, The American Modelica Conference 2018, Cambridge, Massachusetts, United States, 09/10/2018. https://doi.org/10.3384/ecp18154121

ModestPy: An Open-Source Python Tool for Parameter Estimation in Functional Mock-up Units. / Arendt, Krzysztof ; Jradi, Muhyiddine; Wetter, Michael et al.
Proceedings of the 1st American Modelica Conference. ed. / Michael Tiller; Hubertus Tummescheit; Luigi Vanfretti. Modelica Association and Linköping University Electronic Press, 2018. p. 121-130 (Linköping Electronic Conference Proceedings, Vol. 154).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AB - 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.

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KW - parameter estimation

KW - Python

KW - open-source

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Arendt K, Jradi M, Wetter M, Veje C. ModestPy: An Open-Source Python Tool for Parameter Estimation in Functional Mock-up Units. In Tiller M, Tummescheit H, Vanfretti L, editors, Proceedings of the 1st American Modelica Conference. Modelica Association and Linköping University Electronic Press. 2018. p. 121-130. (Linköping Electronic Conference Proceedings, Vol. 154). doi: 10.3384/ecp18154121

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

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