I’m a PhD student at the University of Texas at Austin. I have a multidisciplinary background which includes electrical engineering, energy management and machine learning. I did my master thesis at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and I graduated from the Universidad Pontificia Comillas in 2016, with a M.S. in Industrial Engineering (Electrical Engineering focus). During the summer of 2018 I worked at the National Renewable Energy Laboratory (NREL) developing strategies to achieve 100% renewable cities.
My current topic of research is the use of deep learning to make smart buildings learn from diverse factors such as occupants’ behavior, weather conditions, energy prices, and from each other. My goal is to develop and simulate an AI-algorithm that allows buildings to learn and coordinate among each other, so they avoid consuming energy simultaneously (demand response).
Journal Publications
Vázquez-Canteli, J.R., Ulyanin, S., Kämpf J., and Nagy, Z., “Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities”, Sustainable Cities and Society, 2018.
Vázquez-Canteli, J.R., and Nagy, Z., “Reinforcement Learning for Demand Response: A Review of algorithms and modeling techniques”, Applied Energy 235, 1072-1089, 2019 (published in a special section: Progress in Applied Energy – reserved to the top 3% of the articles).
Leibowicz, B., Lanham, C., Brozynski, M., Vázquez-Canteli, J.R., Castillo-Castejón, N., Nagy, Z., “Optimal decarbonization pathways for urban residential building energy services”, Applied Energy, November 2018
Conferences
Vázquez-Canteli J.R., Kämpf J., and Nagy, Z., “Balancing comfort and energy consumption of a heat pump using batch reinforcement learning with fitted Q-iteration”, CISBAT, Lausanne, 2017 (best paper award)
Vázquez-Canteli, J.R., Ulyanin, S., Kämpf J., and Nagy, Z., “Adaptive Multi-Agent Control of HVAC Systems for Residential Demand Response Using Batch Reinforcement Learning”, ASHRAE/IBPSA 2018, Chicago.
Nagy, Z., Park, J.Y., and Vázquez-Canteli, J.R., “Reinforcement learning for intelligent environments: A Tutorial”, 2018
Nagy, Z., Vázquez-Canteli, J.R., and Park, J.Y., “Using Bluetooth Based Occupancy Estimation for HVAC Set-Back to Reduce Energy Consumption in Buildings”, ASHRAE Conference, Houston, May 2018
Nagy, Z., Park, J.Y., and Vázquez-Canteli, J.R., “Reinforcement Learning for smart buildings and cities” Passive and Low Energy Architecture (PLEA), Edinburgh, 2017
Vázquez-Canteli J.R., Kämpf J. “Energy simulation at the urban scale: a focus on Geneva and climate change scenarios, Sustainable City 2016″; WIT Transactions on Ecology and The Environment, Vol 204.
Master Theses Directed
Nicolas Castillo Castejon, “3D Physical Model of The City of Austin for Energy Building Simulation in Future Scenarios“, 2018
Ignacio Aguirre Panadero, “Electric energy consumer characterization classification and forecasting using tiled convolutional neural networks“, 2018
Ignacio Perez de Rojas, “The impact of climate, geographical location, and human behavior on usage patterns of programmable thermostats”, 2018
Awards & Recognitions
2018 Green Fee Grant, The University of Texas at Austin
2018 Kolodzey travel grant, The University of Texas at Austin
2017 BEST PAPER AWARD – CISBAT 2017 International conference, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
2017 Award of British Petroleum Chair on Energy and Sustainable Development. One of the twelve best projects at Universidad Pontificia Comillas: “Massive 3D Models and Physical Data for building simulation at the urban scale: a focus on Geneva and climate change scenarios” [link, in Spanish]
2010 Excellence Scholarship of the Community of Madrid
