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

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › 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.

Originalsprog	Engelsk
Titel	Proceedings of the 1st American Modelica Conference
Redaktører	Michael Tiller, Hubertus Tummescheit, Luigi Vanfretti
Forlag	Modelica Association and Linköping University Electronic Press
Publikationsdato	okt. 2018
Sider	121-130
ISBN (Elektronisk)	9789176851487
DOI	https://doi.org/10.3384/ecp18154121
Status	Udgivet - okt. 2018
Begivenhed	The American Modelica Conference 2018 - Cambridge, USA Varighed: 9. okt. 2018 → 10. okt. 2018

Konference

Konference	The American Modelica Conference 2018
Land/Område	USA
By	Cambridge
Periode	09/10/2018 → 10/10/2018

Navn	Linköping Electronic Conference Proceedings
Vol/bind	154
ISSN	1650-3686

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10.3384/ecp18154121

Preprint versionAccepteret manuskript, 981 KBLicens: Ikke-specificeret
166522_Modelica_Proceedings_v2Forlagets udgivne version, 31,1 MBLicens: CC BY-NC

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Citationsformater

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. red. / Michael Tiller ; Hubertus Tummescheit ; Luigi Vanfretti. Modelica Association and Linköping University Electronic Press, 2018. s. 121-130 (Linköping Electronic Conference Proceedings, Bind 154).

@inproceedings{319df681660d4b6abfe60d91af19f382,

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",

month = oct,

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. i M Tiller, H Tummescheit & L Vanfretti (red), Proceedings of the 1st American Modelica Conference. Modelica Association and Linköping University Electronic Press, Linköping Electronic Conference Proceedings, bind 154, s. 121-130, The American Modelica Conference 2018, Cambridge, Massachusetts, USA, 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. red. / Michael Tiller; Hubertus Tummescheit; Luigi Vanfretti. Modelica Association and Linköping University Electronic Press, 2018. s. 121-130 (Linköping Electronic Conference Proceedings, Bind 154).

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

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T2 - The American Modelica Conference 2018

AU - Arendt, Krzysztof

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N2 - 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.

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.

KW - FMI

KW - parameter estimation

KW - Python

KW - open-source

UR - https://www.modelica.org/events/modelica2018Americas/ModelicaBostonWorkshops_2018_LR.PDF

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DO - 10.3384/ecp18154121

M3 - Article in proceedings

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BT - Proceedings of the 1st American Modelica Conference

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ER -

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

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

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