House thermal model parameter estimation method for Model Predictive Control applications

Richard Pieter van Leeuwen, J.B. de Wit, J. Fink, Gerardus Johannes Maria Smit

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    5 Citations (Scopus)

    Abstract

    In this paper we investigate thermal network models with different model orders applied to various Dutch low-energy house types with high and low interior thermal mass and containing floor heating. Parameter estimations are performed by using data from TRNSYS simulations. The paper discusses results in relation to model order and the order which yields a sufficient level of accuracy is determined. The paper presents a semi-physical estimation method which is used to improve correlation of model parameters with physical determined values. The thermal network model can be used for various simulation studies or for Model Predictive Control (MPC) of house heating or cooling systems. The paper investigates accuracy of the model for MPC by comparing MPC-results with results from TRNSYS simulations, including ventilation heat losses.
    Original languageUndefined
    Title of host publicationIEEE PowerTech Eindhoven 2015
    Place of PublicationUSA
    PublisherIEEE Power & Energy Society
    Pages1-6
    Number of pages6
    ISBN (Print)978-1-4673-5667-1
    DOIs
    Publication statusPublished - Jun 2015
    EventIEEE PowerTech 2015 - Eindhoven University of Technology, Eindhoven, Netherlands
    Duration: 29 Jun 20152 Jul 2015

    Publication series

    Name
    PublisherIEEE Power & Energy Society

    Conference

    ConferenceIEEE PowerTech 2015
    CountryNetherlands
    CityEindhoven
    Period29/06/152/07/15

    Keywords

    • EWI-26590
    • METIS-315109
    • low-energy house types
    • house thermal model parameter estimation method
    • model predictive control applications
    • Smart Grid
    • Parameter estimation
    • System Identification
    • Thermal Network Model
    • Predictive control
    • Predictive models
    • buildings (structures)
    • interior thermal mass
    • thermal network models
    • Mathematical model
    • Atmospheric modeling
    • Floor Heating
    • Model Predictive Control
    • Data models
    • Heat pumps
    • Heating
    • IR-98716
    • Accuracy
    • ventilation

    Cite this

    van Leeuwen, R. P., de Wit, J. B., Fink, J., & Smit, G. J. M. (2015). House thermal model parameter estimation method for Model Predictive Control applications. In IEEE PowerTech Eindhoven 2015 (pp. 1-6). USA: IEEE Power & Energy Society. https://doi.org/10.1109/PTC.2015.7232335
    van Leeuwen, Richard Pieter ; de Wit, J.B. ; Fink, J. ; Smit, Gerardus Johannes Maria. / House thermal model parameter estimation method for Model Predictive Control applications. IEEE PowerTech Eindhoven 2015. USA : IEEE Power & Energy Society, 2015. pp. 1-6
    @inproceedings{610a4651c96644bbae82665e4026eb52,
    title = "House thermal model parameter estimation method for Model Predictive Control applications",
    abstract = "In this paper we investigate thermal network models with different model orders applied to various Dutch low-energy house types with high and low interior thermal mass and containing floor heating. Parameter estimations are performed by using data from TRNSYS simulations. The paper discusses results in relation to model order and the order which yields a sufficient level of accuracy is determined. The paper presents a semi-physical estimation method which is used to improve correlation of model parameters with physical determined values. The thermal network model can be used for various simulation studies or for Model Predictive Control (MPC) of house heating or cooling systems. The paper investigates accuracy of the model for MPC by comparing MPC-results with results from TRNSYS simulations, including ventilation heat losses.",
    keywords = "EWI-26590, METIS-315109, low-energy house types, house thermal model parameter estimation method, model predictive control applications, Smart Grid, Parameter estimation, System Identification, Thermal Network Model, Predictive control, Predictive models, buildings (structures), interior thermal mass, thermal network models, Mathematical model, Atmospheric modeling, Floor Heating, Model Predictive Control, Data models, Heat pumps, Heating, IR-98716, Accuracy, ventilation",
    author = "{van Leeuwen}, {Richard Pieter} and {de Wit}, J.B. and J. Fink and Smit, {Gerardus Johannes Maria}",
    note = "10.1109/PTC.2015.7232335",
    year = "2015",
    month = "6",
    doi = "10.1109/PTC.2015.7232335",
    language = "Undefined",
    isbn = "978-1-4673-5667-1",
    publisher = "IEEE Power & Energy Society",
    pages = "1--6",
    booktitle = "IEEE PowerTech Eindhoven 2015",

    }

    van Leeuwen, RP, de Wit, JB, Fink, J & Smit, GJM 2015, House thermal model parameter estimation method for Model Predictive Control applications. in IEEE PowerTech Eindhoven 2015. IEEE Power & Energy Society, USA, pp. 1-6, IEEE PowerTech 2015, Eindhoven, Netherlands, 29/06/15. https://doi.org/10.1109/PTC.2015.7232335

    House thermal model parameter estimation method for Model Predictive Control applications. / van Leeuwen, Richard Pieter; de Wit, J.B.; Fink, J.; Smit, Gerardus Johannes Maria.

    IEEE PowerTech Eindhoven 2015. USA : IEEE Power & Energy Society, 2015. p. 1-6.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    TY - GEN

    T1 - House thermal model parameter estimation method for Model Predictive Control applications

    AU - van Leeuwen, Richard Pieter

    AU - de Wit, J.B.

    AU - Fink, J.

    AU - Smit, Gerardus Johannes Maria

    N1 - 10.1109/PTC.2015.7232335

    PY - 2015/6

    Y1 - 2015/6

    N2 - In this paper we investigate thermal network models with different model orders applied to various Dutch low-energy house types with high and low interior thermal mass and containing floor heating. Parameter estimations are performed by using data from TRNSYS simulations. The paper discusses results in relation to model order and the order which yields a sufficient level of accuracy is determined. The paper presents a semi-physical estimation method which is used to improve correlation of model parameters with physical determined values. The thermal network model can be used for various simulation studies or for Model Predictive Control (MPC) of house heating or cooling systems. The paper investigates accuracy of the model for MPC by comparing MPC-results with results from TRNSYS simulations, including ventilation heat losses.

    AB - In this paper we investigate thermal network models with different model orders applied to various Dutch low-energy house types with high and low interior thermal mass and containing floor heating. Parameter estimations are performed by using data from TRNSYS simulations. The paper discusses results in relation to model order and the order which yields a sufficient level of accuracy is determined. The paper presents a semi-physical estimation method which is used to improve correlation of model parameters with physical determined values. The thermal network model can be used for various simulation studies or for Model Predictive Control (MPC) of house heating or cooling systems. The paper investigates accuracy of the model for MPC by comparing MPC-results with results from TRNSYS simulations, including ventilation heat losses.

    KW - EWI-26590

    KW - METIS-315109

    KW - low-energy house types

    KW - house thermal model parameter estimation method

    KW - model predictive control applications

    KW - Smart Grid

    KW - Parameter estimation

    KW - System Identification

    KW - Thermal Network Model

    KW - Predictive control

    KW - Predictive models

    KW - buildings (structures)

    KW - interior thermal mass

    KW - thermal network models

    KW - Mathematical model

    KW - Atmospheric modeling

    KW - Floor Heating

    KW - Model Predictive Control

    KW - Data models

    KW - Heat pumps

    KW - Heating

    KW - IR-98716

    KW - Accuracy

    KW - ventilation

    U2 - 10.1109/PTC.2015.7232335

    DO - 10.1109/PTC.2015.7232335

    M3 - Conference contribution

    SN - 978-1-4673-5667-1

    SP - 1

    EP - 6

    BT - IEEE PowerTech Eindhoven 2015

    PB - IEEE Power & Energy Society

    CY - USA

    ER -

    van Leeuwen RP, de Wit JB, Fink J, Smit GJM. House thermal model parameter estimation method for Model Predictive Control applications. In IEEE PowerTech Eindhoven 2015. USA: IEEE Power & Energy Society. 2015. p. 1-6 https://doi.org/10.1109/PTC.2015.7232335