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Optimisation du contrôle de bâtiments industriels utilisant l’IdO à l’aide de l’apprentissage par renforcement profond.

Optimizing controls of IoT-based manufacturing buildings through deep reinforcement learning.

Numéro : 3579

Auteurs : XU D., SHIN J., ZHAO L., QU M.

Résumé

Maintaining an optimal operating environment within manufacturing facilities is crucial for enhancing energy efficiency, boosting manufacturing productivity, and ensuring occupant comfort and health. With the increasing adoption of Internet of Things (IoT) sensors and cloud-based data acquisition systems in manufacturing facilities, a wealth of IoT
operational data is available. This abundance of data empowers data-driven energy analytics and facilitates intelligent control for building operations. Deep Reinforcement Learning (DRL) control is an emerging intelligent control that leverages building big data and artificial intelligence algorithms to optimize operational efficiency and environmental
conditions. In this work, operational data is collected and streamed from a manufacturing building equipped with IoT sensors. A customized environment for the DRL is constructed using the operational data. Within the environment, building characteristics and heat transfer process are modeled by a Resistor-Capacitor network. The DRL model is
subsequently trained with the Proximal Policy Optimization algorithm to find an optimal control policy. Results show that the DRL building control framework effectively maintains desired indoor conditions in conditioned zones with reduced fluctuation. Moreover, there is a notable decrease in energy consumption, with a demonstrated 33.8% reduction
in energy cost savings over a two-month testing period. The implementation of the DRL method also leads to an estimated annual reduction of 4.80 kg/m2 in carbon emissions for the entire building, therefore contributing to environmental impact mitigation.

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Pages : 10 p.

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Détails

  • Titre original : Optimizing controls of IoT-based manufacturing buildings through deep reinforcement learning.
  • Identifiant de la fiche : 30032906
  • 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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