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Comparaison entre les stratégies courantes de réglage de la température de l’air d’alimentation et celles basées sur l’apprentissage par renforcement avec des préférences des occupants variables en termes de température.

A comparison between common and reinforcement learning-based supply air temperature reset strategies with varying occupant temperature preferences.

Numéro : 3543

Auteurs : ELEHWANY H., GUNAY B., OUF M., COTRUFO N., VENNE J-S., WEN J.

Résumé

The supply air temperature (SAT) of an air handling unit in multi-zone variable air volume systems could impact the energy use significantly. Formerly, buildings used a constant SAT which resulted in high energy consumption due to the increased load on perimeter heaters. Lately, ASHRAE guideline 36 introduced the trim and respond logic (Taylor, 2015) as an improved SAT reset strategy which depends on the feedback of the cooling requests of the zones. However, the trim and respond logic might fail to provide comfort and/or energy savings in case of higher demands, conflicting thermal preferences at different zones and varying occupancy patterns. This study investigates four SAT reset strategies: 1) constant 13℃ SAT, 2) SAT reset based on outdoor air temperature (OAT), 3) trim and respond, and 4) trim and respond combined with OAT reset; with different cases of varying zone setpoints. It also introduces a deep Q-network (DQN) reinforcement learning (RL) algorithm for SAT reset and compares its performance with the other strategies. All the cases are simulated using EnergyPlus. The objective is to address the shortcomings of the currently adopted methods in industry and to show the potential of reinforcement learning in HVAC controls. The results show that the common SAT reset strategies do not perform well with cases of varying setpoint leading to either higher energy cost or decrease in occupant comfort, while the DQN-based method provided a better alternative. These findings establish a basis for future work that would focus on developing a multi-agent occupant centric control (OCC) method that takes energy and occupant comfort into account by utilizing RL methods.

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Pages : 8 p.

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Détails

  • Titre original : A comparison between common and reinforcement learning-based supply air temperature reset strategies with varying occupant temperature preferences.
  • Identifiant de la fiche : 30032914
  • Langues : Anglais
  • Sujet : Technologie
  • Source : 2024 Purdue Conferences. 8th International High Performance Buildings Conference at Purdue.
  • Date d'édition : 15/07/2024

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