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Creators/Authors contains: "Chen, Runfeng"

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  1. ; ; ; ; ; (, IEEE Internet of Things Journal)
    In this work, we present recEnergy, a recommender system for reducing energy consumption in commercial buildings with human-in-the-loop. We formulate the building energy optimization problem as a Markov Decision Process, show how deep reinforcement learning can be used to learn energy saving recommendations, and effectively engage occupants in energy-saving actions. is a recommender system that learns actions with high energy saving potential, actively distribute recommendations to occupants in a commercial building, and utilize feedback from the occupants to learn better energy saving recommendations. Over a four week user study, four different types of energy saving recommendations were trained and learned. improves building energy reduction from a baseline saving (passive-only strategy) of 19% to 26%. 
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