Advanced Control of Dynamic Facades and HVAC with Reinforcement Learning

With increased complexity due to time-variable renewable electricity supply and associated variable cost, it has become evident that management of building energy demand as a resource is essential for grid stabilization. However, techno-economic and human constraints make such solutions non-trivial. Reinforcement learning (RL), a discipline of machine learning, was explored to take on this challenge. The open-source Functional Mock-up Interface - Machine Learning Center (FMI-MLC) was developed to provide a standardized interface of RL and simulation environment, through the FMI industry standard for co-simulation. RL demonstrated its ability to operate an electrochromic window and heating, ventilation, and air conditioning system (HVAC), and reduce demand during critical periods of the electric power grid.