Smart Meter Data Analytics: Practical Use-Cases and Best Practices of Machine Learning Applications for Energy Data in the Residential Sector

A practical guide to current trends in smart meter data analytics with a focus on feature engineering and machine learning scenarios for energy data at 15-minute resolution. Gain insights into current trends and use cases in the energy field and get a sense of typical and atypical energy consumption in a residential building.

Authors:

• Tobias Brudermüller (Brudermueller), Bits to Energy Lab, ETH Zurich, tbrudermuell@ethz.ch
• Markus Kreft, Bits to Energy Lab, ETH Zurich, mkreft@ethz.ch

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.

Estimated time to execute end-to-end: 20 minutes

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

Brudermueller, T., & Kreft, M. (2023). Smart Meter Data Analytics: Practical Use-Cases and Best Practices of Machine Learning Applications for Energy Data in the Residential Sector [Tutorial]. In International Conference on Learning Representations. Climate Change AI. https://doi.org/10.5281/zenodo.11623001

BibTeX

@misc{brudermueller2023smart,
  title={Smart Meter Data Analytics: Practical Use-Cases and Best Practices of Machine Learning Applications for Energy Data in the Residential Sector},
  author={Brudermueller, Tobias and Kreft, Markus},
  year={2023},
  organization={Climate Change AI},
  type={Tutorial},
  doi={https://doi.org/10.5281/zenodo.11623001},
  booktitle={International Conference on Learning Representations},
  howpublished={\url{https://github.com/climatechange-ai-tutorials/smart-meter-analytics}}
}