CityLearn V2beta released!

We’re pleased to announce 📣 the launch of CityLearn V2beta. This new release is a significant step towards realizing energy flexibility in smart buildings 🏙️. Check it out at 👉

CityLearn is an international open-source community-driven Open AI Gym environment to study grid-interactive smart communities. With the implementation of advanced control systems (MPC, RL), we aim to improve building energy coordination and demand response in cities, and significantly reduce related greenhouse gas emissions 🌐

CityLearn V2beta offers exciting new features: from integration with DOE’s RESSTOCK and EULP databases 🏘️ for energy models of buildings and distributed energy resources, to introducing LSTM models for dynamic building thermal environment 🌡️.

👏 Big shoutout to the core team Kingsley Nweye (The University of Texas at AustinKathryn Kaspar (Concordia University) and Giacomo Buscemi (Politecnico di Torino) Their contributions have been crucial to this update 👏.

There is a long list of other contributors without whom this upgrade wouldn’t have happened: Big thanks to (in no particular order): Alfonso Capozzoli Allen Wu Aysegul Demir Dilsiz Calvin Lin Dipam ChakrabortyGiuseppe Pinto Gregor Henze Han Li Hyun Park Ján Drgoňa José Ramón Vázquez Canteli June Young Park Kaitlyn Ng Ludwig BaldMohamed Ouf Sharada Prasanna Mohanty Siva SankaranarayananSourav Dey Tianzhen Hong Yara Almilaify, EIT

Join us in this journey towards transforming energy consumption. Welcome to CityLearn 🚀. #CityLearnV2beta #EnergyFlexibility#OpenSource #SmartBuildings #GridInterActiveBuildings#reinforcementlearning #decarbonization #smartcities #ai#community #building #energy #environment 

👉 Stay tuned for the CityLearn Challenge 2023 !!

Zoltan Nagy
Zoltan Nagy
Assistant Professor

My research interests include reinforcement learning for buildings and smart cities.