I’m a postdoctoral researcher at the University of Texas at Austin. I received my PhD in August 2020 by The University of Texas at Austin, and I have a multidisciplinary background in electrical engineering, energy management and machine learning. I did my master thesis at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and graduated from Universidad Pontificia Comillas in 2016. I’ve worked at the National Renewable Energy Laboratory (NREL) developing strategies to achieve 100% renewable cities, and at Siemens, developing state-of-the-art anomaly detection and fault diagnostics systems for smart buildings.
My topic of research is the use of reinforcement learning for demand response. My objective is to design communities of smart buildings capable of learning from each other, their occupants, and external conditions to flatten the overall curve of electricity by providing a coordinated response. Check out my CityLearn Challenge.
Email: jose.vazquezcanteli@utexas.edu
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
Vazquez-Canteli, J., G. Henze, and Nagy, Z., “MARLISA: Multi-Agent Reinforcement Learning with Iterative Sequential Action Selection for Load Shaping of Grid-Interactive Connected Buildings”, ACM BuildSys Yokohama, Japan, 2020
Vázquez-Canteli, J.R., et al. “Multi-Agent Reinforcement Learning for Adaptive Demand Response in Smart Cities”, CISBAT, Lausanne, Switzerland, 2019.
Vázquez-Canteli, J.R., et al. “Deep neural networks as surrogate models for urban energy simulations”, CISBAT, Lausanne, Switzerland, 2019. SILVER PAPER AWARD
Vázquez-Canteli, J.R., et al. “CityLearn v1.0: An OpenAI Gym Environment for Demand Response with Deep Reinforcement Learning”, BuildSys, p. 356-357, New York City, 2019
Ulyanin, S., Vázquez-Canteli, J.R., et al. “Feature extraction and clustering of building energy profiles encoded as images”, Building Simulation, Rome, Italy, 2019.
Felkner, J., Brown, J., Vázquez-Canteli, J.R., et al. “Urban Densification and Housing Typology for Climate Change Mitigation”, CISBAT, Lausanne, Switzerland, 2019.
Ulyanin, S., Vázquez-Canteli, J.R., et al. “SCAFE: Automated simultaneous clustering and non-linear feature extraction of building energy profiles”, CISBAT, Lausanne, Switzerland, 2019.
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
2020 Highly Cited Paper Award – Applied Energy
2019 SILVER PAPER AWARD – CISBAT 2019 International conference, EPFL, Switzerland
2019 Travel Grant & Guest Speaker at Rosenfeld Symposium, Lawrence Berkeley National Laboratory
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
