Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 7, Issue 7, Pages 6402-6413Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.2974848
Keywords
Buildings; Energy consumption; Recommender systems; Learning (artificial intelligence); Optimization; Machine learning; Internet of Things; Building energy optimization; deep reinforcement learning; energy savings; recommender system
Categories
Funding
- National Science Foundation [CNS-1704899, CNS-1815274, CNS-1943396]
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In this article, 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. recEnergy is a recommender system that learns actions with high-energy-saving potential, actively distributes recommendations to occupants in a commercial building, and utilizes 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. recEnergy improves building energy reduction from a baseline saving (passive-only strategy) of 19%-26%.
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