Publication

We collected a building energy dataset (2 years, hourly data) of unprecedented size (3829 buildings) and variety (75 programs). And using machine learning, we discovered three fundamental load shape profiles that characterize the temporal energy use in any of the buildings. The existence of fundamental load shape profiles challenges the… read more 

The journal Sustainable Cities and Society has published our paper Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities led by our awesome PhD student Jose. We have developed CitySim, a framework to study multi-agent reinforcement learning using state-of-the art machine learning tools (TensorFlow) integrated with… read more 

Our paper Reinforcement learning for demand response: A review of algorithms and modeling techniques led by IEL’s PhD student Jose has been published in Applied Energy. We’re thrilled that it has been selected by the editors to be included into the Special Section Progress in Applied Energy. J.R. Vazquez-Canteli and Z.… read more