RecEnergy Accepted to IEEE Internet of Things Journal

Our paper titled “A Deep Reinforcement Learning Based Recommender System for Occupant-Driven Energy Optimization in Commercial Buildings” has been accepted for publication in the IEEE Internet of Things Journal. The paper introduces a recommender system for reducing energy consumption in commercial buildings with human-in-the-loop. We show how deep reinforcement learning can be used to learn and send energy-saving recommendations to effectively engage building occupants to perform more energy-saving actions.

Congratulations to Peter Wei and the rest of team!