Very basic examples showing how you can use OpenSTEF in a jupyter notebook on your local machine.
You can also use Binder to explore the notebooks in an online, interactive environment.
The following example notebooks are available:
- Train a model using high-level pipelines
- Train a model and perform a backtest
- Train a model and make a forecast
- Split net load into components using DAZLs
- Obtain derived features
- Analyzing perturbed inputs
Install:
pip install -r requirements.txt
Run:
jupyter notebook
This project is licensed under the Mozilla Public License, version 2.0 - see LICENSE for details.
This project includes third-party libraries, which are licensed under their own respective Open-Source licenses. SPDX-License-Identifier headers are used to show which license is applicable. The concerning license files can be found in the LICENSES directory.
Please read CODE_OF_CONDUCT.md, CONTRIBUTING.md and PROJECT_GOVERNANACE.md for details on the process for submitting pull requests to us.
Please read SUPPORT.md for how to connect and get into contact with the OpenSTEF project