Partager

Optimisation énergétique rapide des systèmes à compression de vapeur à l'aide de l'apprentissage automatique probabiliste et de la régulation par recherche d'extremum.

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

Numéro : 2411

Auteurs : CHAKRABARTY A., BURNS D. J., GUAY M., LAUGHMAN C. R.

Résumé

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.

Documents disponibles

Format PDF

Pages : 10 p.

Disponible

Gratuit

Télécharger

Détails

  • Titre original : Rapid energy optimization of vapor compression systems using probabilistic machine learning and extremum seeking control.
  • Identifiant de la fiche : 30030717
  • Langues : Anglais
  • Sujet : Technologie
  • Source : 2022 Purdue Conferences. 19th International Refrigeration and Air-Conditioning Conference at Purdue.
  • Date d'édition : 2022

Liens


Voir d'autres communications du même compte rendu (224)
Voir le compte rendu de la conférence

Indexation