Pursuing Energy Saving and Thermal Comfort With a Human-Driven DRL Approach

Abstract

The management of thermal comfort in a building is a challenging and multifaced problem, because the use of objective parameters, for example, the energy consumption, should be combined with subjective requirements, related to human profile and preferences. This article exploits cognitive technologies, based on deep reinforcement learning (DRL), for the automatic control of the heating, ventilation, and air conditioning system in an office. The learning process is driven by a reward that includes multiple components, related to energy consumption, indoor temperature, and user perceptions, which are inferred by the human interactions with the system. This approach is inspired by the human-in-the-loop paradigm, which in our case helps the DRL controller to learn the requirements of users and readily adapt to them. Experimental results show that the appropriate balance of the reward components can be efficiently exploited to give the desired importance to the different objectives.