Energy Forecast Benchmark Toolkit
Energy Forecast Benchmark Toolkit is a Python project that aims to provide common tools to
benchmark forecast models.
Documentation: https://attila-balint-kul.github.io/energy-forecast-benchmark-toolkit/
Datasets
Dashboards
Table of Contents
Installation
Use the package manager pip to install foobar.
pip install enfobench
Usage
Download the HuggingFace Dataset 'EDS-lab/electricity-demand',
and download the files from the data folder to your computer.
import pandas as pd
from enfobench import Dataset
from enfobench.datasets import ElectricityDemandDataset
from enfobench.evaluation import cross_validate, evaluate_metrics
from enfobench.evaluation.metrics import mean_bias_error, mean_absolute_error, root_mean_squared_error
ds = ElectricityDemandDataset("/path/to/the/dataset/folder/that/contains/all/subsets")
ds.list_unique_ids()
unique_id = ds.list_unique_ids()[0]
target, past_covariates, metadata = ds.get_data_by_unique_id(unique_id)
dataset = Dataset(
target=target,
past_covariates=past_covariates,
future_covariates=None,
metadata=metadata
)
model = MyForecastModel()
cv_results = cross_validate(
model,
dataset,
start_date=pd.Timestamp("2018-01-01"),
end_date=pd.Timestamp("2018-01-31"),
horizon=pd.Timedelta("24 hours"),
step=pd.Timedelta("1 day"),
)
metrics = evaluate_metrics(
cv_results,
metrics={
"MBE": mean_bias_error,
"MAE": mean_absolute_error,
"RMSE": root_mean_squared_error,
},
)
To get started with some examples check out the models
folder and the examples section of the documentation.
Benchmarking
Once confident in your model, you can submit for evaluation.
The results of the benchmarks are openly accessible through various dashboards. The links you can find above.
Contributing
Contributions and feedback are welcome! For major changes, please open an issue first to discuss
what you would like to change.
If you'd like to contribute to the project, please follow these steps:
Fork the repository.
Create a new branch for your feature or bug fix.
Make your changes and commit them.
Push your changes to your forked repository.
Submit a pull request describing your changes.
License
BSD-3-Clause license