Recent research efforts have made significant progress in reducing commercial building energy consumption through a variety of methods, including optimizing building heating, ventilation, and air conditioning (HVAC), lighting, and personal electric devices. However, these works focus on reducing energy consuming resources while treating occupants as immovable objects separate from the building energy optimization problem.
To address this, we developed 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.
IEEE Internet of Things Journal
A Deep Reinforcement Learning Based Recommender System for Occupant-Driven Energy Optimization in Commercial Buildings
Peter Wei, Stephen Xia, Runfeng Chen, Jingyi Qian, Chong Li, Xiaofan Jiang
ACM User Modeling, Adaptation, and Personalization (UMAP 2018)
Energy Saving Recommendations and User Location Modeling in Commercial Buildings
Peter Wei, Stephen Xia, Xiaofan Jiang