TR2022-172
Rapid Energy Optimization of Vapor Compression Systems Using Probabilistic Machine Learning and Extremum Seeking Control
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- "Rapid Energy Optimization Of Vapor Compression Systems Using Probabilistic Machine Learning And Extremum Seeking Control", International Refrigeration and Air Conditioning Conference (IRACC), July 2022.BibTeX TR2022-172 PDF
- @inproceedings{Chakrabarty2022jul,
- author = {Chakrabarty, Ankush and Burns, Daniel J. and Guay, Martin and Laughman, Christopher R.},
- title = {Rapid Energy Optimization Of Vapor Compression Systems Using Probabilistic Machine Learning And Extremum Seeking Control},
- booktitle = {International Refrigeration and Air Conditioning Conference (IRACC)},
- year = 2022,
- month = jul,
- url = {https://www.merl.com/publications/TR2022-172}
- }
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- "Rapid Energy Optimization Of Vapor Compression Systems Using Probabilistic Machine Learning And Extremum Seeking Control", International Refrigeration and Air Conditioning Conference (IRACC), July 2022.
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MERL Contacts:
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Research Areas:
Abstract:
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.
Related Publication
- @article{Chakrabarty2022nov2,
- author = {Chakrabarty, Ankush and Burns, Daniel J. and Guay, Martin and Laughman, Christopher R.},
- title = {Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization},
- journal = {Journal of Process Control},
- year = 2022,
- volume = 120,
- pages = {86--96},
- month = nov,
- doi = {10.1016/j.jprocont.2022.11.006},
- url = {https://www.merl.com/publications/TR2022-144}
- }