climatechange-ai-tutorials / citylearn

Learn how to design simple and advanced control algorithms to provide energy flexibility, and acquire familiarity with the CityLearn environment and its datasets for extended use in projects. The tutorial provides a walk-through on how to set up and interact with the environment using a real-world dataset in three hands-on control experiments.

CityLearn: Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities

Learn how to design simple and advanced control algorithms to provide energy flexibility, and acquire familiarity with the CityLearn environment and its datasets for extended use in projects. The tutorial provides a walk-through on how to set up and interact with the environment using a real-world dataset in three hands-on control experiments.

Authors:

• Kingsley Nweye, The University of Texas at Austin, [e-mail]
• Allen Wu, The University of Texas at Austin, [e-mail]
• Hyun Park, The University of Texas at Austin, [e-mail]
• Yara Almilaify, The University of Texas at Austin, [e-mail]
• Zoltan Nagy, The University of Texas as Austin, [e-mail]

Originally presented at ICLR 2023

Access this tutorial

We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.

We estimate that this tutorial will take around 20 minutes to execute from end-to-end.

Contribute to this tutorial

Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.

Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.

Climate Change AI Tutorials

Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.

License

Usage of this tutorial is subject to the MIT License.

Cite

Plain Text

Nweye, K., Wu, A., Almilaify, Y., & Nagy, Z. (2024). CityLearn: Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.11639022

BibTeX

@misc{nweye2024citylearn:,
  title={CityLearn: Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities},
  author={Nweye, Kingsley and Wu, Allen and Almilaify, Yara and Nagy, Zoltan},
  year={2024},
  organization={Climate Change AI},
  type={Tutorial},
  doi={https://doi.org/10.5281/zenodo.11639022},
  booktitle={Climate Change AI Summer School},
  howpublished={\url{https://https://github.com/climatechange-ai-tutorials/citylearn}}
}