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Rapid energy optimization of vapor compression systems using probabilistic machine learning and extremum seeking control.

Number: 2411

Author(s) : CHAKRABARTY A., BURNS D. J., GUAY M., LAUGHMAN C. R.

Summary

Extremum seeking control (ESC) is a popular datadriven approach for optimizing the energy consumption of vapor compression systems (VCS). Tuning ESC control parameters can present a challenge to implementation, especially in advanced variants of ESC, because timeconsuming and problemspecific manual tuning is often required to eliminate numerical and dynamical instabilities. In this paper, we propose an automatic ESC tuning mechanism based on a Bayesian optimization framework that systematically leverages closedloop ESC experiments to compute highperforming ESC parameters. We validate the proposed Bayesianoptimized ESC on a physicsbased Modelica model of a VCS. This new approach is six times faster and yields a 9% higher coefficient of performance than a stateoftheart timevarying ESC method under identical experimental conditions.

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Pages: 10 p.

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  • Original title: Rapid energy optimization of vapor compression systems using probabilistic machine learning and extremum seeking control.
  • Record ID : 30030717
  • Languages: English
  • Subject: Technology
  • Source: 2022 Purdue Conferences. 19th International Refrigeration and Air-Conditioning Conference at Purdue.
  • Publication date: 2022

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