This repository provides an implementation of a robustness and reliability evaluation for energy forecasting models, aligned with the requirements of the EU AI Act. The model is evaluated using the Giskard module. A detailed description of the implemented tests and their mapping to relevant EU AI Act requirements can be found in app/main.py.
To deploy the servie - run the following command; it will be avalible at http://0.0.0.0:8501
docker-compose up --buildThe Dashboard is orginized by chosing existing submission_id or typying a new one. Models are uploaded as ONNX checkpoints (.onnx) — PyTorch, XGBoost, scikit-learn, TensorFlow, etc. can all be exported to ONNX and evaluated through the same pipeline. On the main tab, there is a box to submit the ONNX checkpoint. Example training scripts that export to ONNX can be found in example_xgboost, example_torch folders and in demonstration_preparation.ipynb. To submit data, choice your dataframe saved as csv file and select target columns. To generate reports of model and data, switch to correspinding tab, configure ieeebus39 if needed and push the button.
The repository includes example files demonstrating the full workflow. The sample dataset corresponds to electricity consumption forecasting for a new england zone, based on data from:
https://www.iso-ne.com/isoexpress/web/reports/load-and-demand/-/tree/zone-info
This example illustrates how the system can be used to assess energy forecasting models under regulatory-oriented robustness and reliability criteria. The data and checkpoints for models are stored in example_xgboost and example_torch folders.
The authors would like to acknowledge funding from the European Union's Horizon Europe Framework Programme EnerTEF project under Grant Agreement No. 101172887.