Building Load Forecasting with Machine Learning

Accurate forecasts of energy demand and supply are essential to mitigate climate change. Discover how to train and evaluate building load forecasts using off-the-shelf ML models.

Author:

• Marcus Voss: voss.marcus@gmail.com – Birds on Mars, Technische Universität Berlin, Climate Change AI

Initially presented at Climate Change AI Summer School 2022

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: 5 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

Voss, M. (2024). Building Load Forecasting with Machine Learning [Tutorial]. In Climate Change AI Summer School 2024. Climate Change AI. https://doi.org/10.5281/zenodo.11553722

BibTeX

@misc{voss2024building,
  title={Building Load Forecasting with Machine Learning},
  author={Voss, Marcus},
  year={2024},
  organization={Climate Change AI},
  type={Tutorial},
  doi={https://doi.org/10.5281/zenodo.11553722},
  booktitle={Climate Change AI Summer School 2024},
  howpublished={\url{https://github.com/climatechange-ai-tutorials/building-load-forecasting}}
}