RLEM'21 Workshop at ACM BuildSys'21

Buildings account for 40% of the global energy consumption and 30% of the associated greenhouse gas emissions, while also offering a 50–90% CO2 mitigation potential. The transportation sector is responsible for an additional 30%. Optimal decarbonization requires electrification of end-uses and concomitant decarbonization of electricity supply, efficient use of electricity for lighting, space heating, cooling and ventilation (HVAC), and domestic hot water generation, and upgrade of the thermal properties of buildings. A major driver for decarbonization are integration of renewable energy systems (RES) into the grid, and photovoltaics (PV) and solar-thermal collectors as well as thermal and electric storage into residential and commercial buildings. Electric vehicles (EVs), with their storage capacity and inherent connectivity, hold a great potential for integration with buildings. 

The integration of these technologies must be done carefully to unlock their full potential. Artificial intelligence is regarded as a possible pathway to orchestrate these complexities of Smart Cities. In particular, (deep) reinforcement learning algorithms have seen an increased interest and have demonstrated human expert level performance in other domains, e.g., computer games. Research in the building and cities domain has been fragmented and with focus on different problems and using a variety of frameworks. The purpose of this Workshop is to build a growing community around this exciting topic, provide a platform for discussion for future research direction, and share common frameworks.

Abstracts due 8/16/2021
Paper submission 8/23/2021
more on www.rlem-workshop.net
The workshop will be virtual/hybrid

Topics of Interest

Topics of interest include, but are not limited to:

  • Challenges and Opportunities for RL in Building and Cities
  • Explorations of model vs model-free RL algorithms and hybrids
  • Comparisons of RL algorithms to other control solutions, e.g., model-predictive control
  • Frameworks and datasets for benchmarking algorithms
  • Theoretical contributions to the RL field brought about by constraints/challenges in the buildings/cities domain
  • Applications (demand response, HVAC control, occupant integration, traffic scheduling, EV/battery charging, DER integration)
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Hi :)

Hi everyone, I'm Zoltan, I am faculty at The University of Texas at Austin, directing the intelligent environments laboratory (IEL). 

I research on smart buildings and cities, specifically building energy management. My group released the CityLearn environment to study reinforcement learning algorithms in their ability to manage loads in groups of buildings for demand response applications (load shifting and shaping, etc). We also organize the annual CityLearn Challenge competition, check it out on www.citylearn.net (link to the github environment is in there).

I am also organizing the ACM SIGEnergy RLEM workshop dealing with the topic of RL in infrastructure management, take a look at rlem-workshop.net.

Good to be here at CCAI, looking forward to good discussions :)
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