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Assessing Steady-State, Multivariate Experimental Data Using Gaussian Processes: The GPExp Open-Source Library

Author

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  • Sylvain Quoilin

    (Energy Systems Research Unit (B49), University of Liège, Sart-Tilman, Liège 4000, Belgium
    Institute for Energy and Transport, European Commission DG Joint Research Centre, P.O. Box 2, Petten NL-1755 ZG, The Netherlands)

  • Jessica Schrouff

    (Laboratory of Behavioral and Cognitive Neuroscience, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
    Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK)

Abstract

Experimental data are subject to different sources of disturbance and errors, whose importance should be assessed. The level of noise, the presence of outliers or a measure of the “explainability” of the key variables with respect to the externally-imposed operating condition are important indicators, but are not straightforward to obtain, especially if the data are sparse and multivariate. This paper proposes a methodology and a suite of tools implementing Gaussian processes for quality assessment of steady-state experimental data. The aim of the proposed tool is to: (1) provide a smooth (de-noised) multivariate operating map of the measured variable with respect to the inputs; (2) determine which inputs are relevant to predict a selected output; (3) provide a sensitivity analysis of the measured variables with respect to the inputs; (4) provide a measure of the accuracy (confidence intervals) for the prediction of the data; (5) detect the observations that are likely to be outliers. We show that Gaussian processes regression provides insightful numerical indicators for these purposes and that the obtained performance is higher or comparable to alternative modeling techniques. Finally, the datasets and tools developed in this work are provided within the GPExp open-source package.

Suggested Citation

  • Sylvain Quoilin & Jessica Schrouff, 2016. "Assessing Steady-State, Multivariate Experimental Data Using Gaussian Processes: The GPExp Open-Source Library," Energies, MDPI, vol. 9(6), pages 1-16, May.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:6:p:423-:d:71083
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    References listed on IDEAS

    as
    1. Quoilin, Sylvain & Lemort, Vincent & Lebrun, Jean, 2010. "Experimental study and modeling of an Organic Rankine Cycle using scroll expander," Applied Energy, Elsevier, vol. 87(4), pages 1260-1268, April.
    2. Quoilin, Sylvain & Broek, Martijn Van Den & Declaye, Sébastien & Dewallef, Pierre & Lemort, Vincent, 2013. "Techno-economic survey of Organic Rankine Cycle (ORC) systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 168-186.
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    Cited by:

    1. Dickes, Rémi & Dumont, Olivier & Daccord, Rémi & Quoilin, Sylvain & Lemort, Vincent, 2017. "Modelling of organic Rankine cycle power systems in off-design conditions: An experimentally-validated comparative study," Energy, Elsevier, vol. 123(C), pages 710-727.
    2. Lecompte, Steven & Gusev, Sergei & Vanslambrouck, Bruno & De Paepe, Michel, 2018. "Experimental results of a small-scale organic Rankine cycle: Steady state identification and application to off-design model validation," Applied Energy, Elsevier, vol. 226(C), pages 82-106.
    3. Balderrama, Sergio & Lombardi, Francesco & Stevanato, Nicolo & Peña, Gabriela & Colombo, Emanuela & Quoilin, Sylvain, 2021. "Surrogate models for rural energy planning: Application to Bolivian lowlands isolated communities," Energy, Elsevier, vol. 232(C).

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