OCTOPUS: Deep Reinforcement Learning for Holistic Smart Building Control

Abstract

Recently, significant efforts have been done to improve quality of comfort for commercial buildings' users while also trying to reduce energy use and costs. Most of these efforts have concentrated in energy efficient control of the HVAC (Heating, Ventilation, and Air conditioning) system, which is usually the core system in charge of controlling buildings' conditioning and ventilation. However, in practice, HVAC systems alone cannot control every aspect of conditioning and comfort that affects buildings' occupants. Modern lighting, blind and window systems, usually considered as independent systems, when present, can significantly affect building energy use, and perhaps more importantly, user comfort in terms of thermal, air quality and illumination conditions. For example, it has been shown that a blind system can provide 12%~35% reduction in cooling load in summer while also improving visual comfort. In this paper, we take a holistic approach to deal with the trade-offs between energy use and comfort in commercial buildings. We developed a system called OCTOPUS, which employs a novel deep reinforcement learning (DRL) framework that uses a data-driven approach to find the optimal control sequences of all building's subsystems, including HVAC, lighting, blind and window systems. The DRL architecture includes a novel reward function that allows the framework to explore the trade-offs between energy use and users' comfort, while at the same time enable the solution of the high-dimensional control problem due to the interactions of four different building subsystems. In order to cope with OCTOPUS's data training requirements, we argue that calibrated simulations that match the target building operational points are the vehicle to generate enough data to be able to train our DRL framework to find the control solution for the target building. In our work, we trained OCTOPUS with 10-year weather data and a building model that is implemented in the EnergyPlus building simulator, which was calibrated using data from a real production building. Through extensive simulations we demonstrate that OCTOPUS can achieve 14.26% and 8.1% energy savings compared with the state-of-the art rule-based method in a LEED Gold Certified building and the latest DRL-based method available in the literature respectively, while maintaining human comfort within a desired range.