OpenSTEF provides automated machine learning pipelines to deliver accurate, self-correcting and explainable forecasts of the load on the grid for the next 48 hours.
Description
The energy transition poses new challenges to all parties in the energy sector. For grid operators, the rise in renewable energy and electrification of energy consumption leads to the capacity of the grid to near its physical constraints. Forecasting the load on the grid in the next hours to days is essential for anticipating on local congestion and making the most of existing assets.
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Screenshot of the operational dashboard showing the key functionality of OpenSTEF
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Documentation
Technical documentation can be found here
Roadmap
The roadmap can be found here
Technical steering committee
More information on the OpenSTEF Technical steering committee can be found here.
Repositories
| Link | Description |
|---|---|
| OpenSTEF | Automated Machine Learning Pipelines. Builds the Open Short Term Forecasting package |
| OpenSTEF-dbc | Provides (company specific) database connector for the OpenSTEF package |
| OpenSTEF-offline-example | Provides Jupyter Notebooks showing how to use OpenSTEF and apply it's functionality to your usecase |
| OpenSTEF-reference | Deploy the entire OpenSTEF stack on your machine. Provides a reference implementation of the OpenSTEF stack |
License
This project is licensed under the Mozilla Public License, version 2.0.
Contributing
Please read CODE_OF_CONDUCT.md, CONTRIBUTING.md and PROJECT_GOVERNANACE.md for details on the process for submitting pull requests to us.
Contact
Please read SUPPORT.md for how to connect and get into contact with the OpenSTEF project