@proceedings{35331,
  author = {Christoph Gehbauer and Andreas S Rippl and Eleanor S Lee},
  title = {Advanced Control of Dynamic Facades and HVAC with Reinforcement Learning},
  abstract = {<p>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.</p>},
  year = {2022},
  journal = {Building Simulation 2021},
  publisher = {IBPSA},
  address = {Bruges},
  language = {eng},
}