0
  • DE
  • EN
  • FR
  • Internationale Datenbank und Galerie für Ingenieurbauwerke

Anzeige

Using Deep Learning To Optimize Hvac Systems in Residential Buildings

Autor(en):

Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Journal of Green Building, , n. 1, v. 19
Seite(n): 29-50
DOI: 10.3992/jgb.19.1.29
Abstrakt:

HVAC systems are crucial for maintaining indoor temperature and humidity in buildings but consume significant energy, accounting for over 50% of a building’s energy use. This study proposes a deep reinforcement learning (DRL) algorithm for optimizing energy consumption in residential building HVAC systems while maintaining occupant comfort. Climate data was collected using low-cost sensors, and a co-simulation framework was developed for offline training and validation of our DRL-based algorithm. The proposed DRL-based algorithm was compared to a rule-based HVAC system regarding energy consumption and occupant comfort. Results show that the proposed algorithm can reduce energy consumption by up to 15% compared to the rule-based HVAC system. DRL is a suitable approach for optimizing HVAC systems due to its ability to adapt to the dynamics of multi-parameterized systems. This study contributes to sustainable building design by proposing a DRL-based algorithm to reduce energy consumption while maintaining a comfortable indoor temperature. Using low-cost sensors and a co-simulation framework provides a practical and cost-effective method for training and validating the proposed algorithm.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.3992/jgb.19.1.29.
  • Über diese
    Datenseite
  • Reference-ID
    10775189
  • Veröffentlicht am:
    29.04.2024
  • Geändert am:
    29.04.2024
 
Structurae kooperiert mit
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine