We have investigated experimentally the use of Reinforcement Learning for lighting control in our own offices. The results are awesome and now published in Building & Environment. J. Park, T. Dougherty, H. Fritz, Z. Nagy, LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning, Building and Environment,2018,… read more
Publication
Optimal decarbonization pathways for urban residential building energy services
Our paper in collaboration with Prof. Leibowicz has been accepted for publication in Applied Energy. It address decarbonization pathways of residential building systems using Austin, TX as an example. Leibowicz, B.D., Lanham, C.M., Brozynski, M.T.Vázquez-Canteli, J.R., Castillo, N., and Nagy, Z. Optimal decarbonization pathways for urban residential building energy services, Applied Energy, Vol.… read more
Chapter in Handbook of Sustainable and Resilient Infrastructure
We have published a Tutorial on Reinforcement Learning (RL) in the forthcoming Handbook of Sustainable and Resilient Infrastructure published by Routledge. We argue that model-free algorithms, such as RL, are particularly useful for controlling buildings systems (HVAC, lighting, etc), because they adapt themselves to the individual characteristics of a building, while… read more