A Deep-Reinforcement-Learning-Based Recommender System for Occupant-Driven Energy Optimization in Commercial Buildings

Abstract

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%.