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Détection d'occupation basée sur un réseau neuronal artificiel pour la régulation des systèmes CVC à haute efficacité énergétique : étude de cas.

ANN-based occupancy detection for energy efficient HVAC control: a case study.

Auteurs : ADHIKARY P., BANDYOPADHYAY S., MAZUMDAR A.

Résumé

Current building climate control systems often rely on pre-determined maximum occupancy numbers coupled with temperature sensor data to regulate heating, ventilation, and air conditioning (HVAC). However, rooms and zones in a building are not always fully occupied. Real-time occupancy information can potentially be used to reduce energy consumption. The paper proposes an ANN-based occupancy detection solution to address the need for real-time in-building occupancy information. The proposed solution can track real-time location of tagged occupants. Based on the findings, detailed and operable strategies for optimizing HVAC related building energy consumption by using occupancy information are proposed. Good agreement was found between the simulated results and obtained project data.

Documents disponibles

Format PDF

Pages : 4

Disponible

  • Prix public

    20 €

  • Prix membre*

    15 €

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Détails

  • Titre original : ANN-based occupancy detection for energy efficient HVAC control: a case study.
  • Identifiant de la fiche : 30024771
  • Langues : Anglais
  • Source : Proceedings of the International Conference on Emerging Technologies for Sustainable and Intelligent HVAC&R Systems, Kolkata, July 27-28 2018.
  • Date d'édition : 27/07/2018

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