Bill2Watt repository contains a Python package called bill2watt; whcih focuses on data-driven prediction of hourly load profiles from time-of-use energy bills. Additionally, the repository contains some show-case examples of how to use the package and data that can be used for testing various predictors (see licensing details here).

The bill2watt package provides functionality for training data-driven models to predict hourly load profiles of heterogeneous and numerous electricity end users. It leverages the time-of-use energy bill, which offers valuable insights into the consumption patterns of the end users. The package implements multiple models to effectively map energy bills to their corresponding load profiles, including standard load profiles, classification, and regression.

In addition to the package, the repository includes an exploratory data analysis (EDA) notebook (eda.ipynb) that demonstrates data analysis techniques and provides insights into the dataset's characteristics and electricity consumption patterns. The EDA notebook serves as an example of how to explore the data related to load profiles. Moreover a notebook to test the various predictors in bill2watt is provided (tests.ipynb), that demonstrates how the predictors can be used on a training/testing dataset and compares the performance of the various predictors according to multiple metrics. The testing notebook can be used to have some insight on how the package 'bill2watt' works.

• Usage
• License
• Contact

To use the bill2watt package, follow these steps:

1. Download or clone the repository to your local machine.
2. Refer to the provided documentation (available here) to learn how to utilize the bill2watt package for predicting load profiles from time-of-use energy bills.
3. Run the Jupyter Notebook file eda.ipynb to access the exploratory data analysis (EDA) notebook and explore the dataset used for load profile analysis.
4. Run the Jupyter Notebook file tests.ipynb to evaluate the various predictors in the bill2watt package and compare their performances based on various metrics.
5. Additional examples and guides on using the bill2watt package will be provided soon.

If you have any questions or need further assistance, please contact the project author.

Thank you!

• Code: Creative Commons Attribution 4.0 International (CC BY 4.0)
• Data: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

The code in the bill2watt package and in the rest of the repository is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that you are free to use, modify, and distribute the code for any purpose, including commercial use, as long as you provide attribution to the original author(s), by citing the following reference:

P. Lazzeroni, G. Lorenti and M. Repetto, "A data-driven approach to predict hourly load profiles from time-of-use electricity bills," in IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3286020. [Crossref].

The data contained in the CSV files throughout the repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. This means that you are free to use the data for academic purposes, such as research and educational activities, but you are not allowed to use it for commercial purposes without obtaining explicit permission. Please note that the data contained in the CSV files should be referenced as follows:

P. Lazzeroni, G. Lorenti and M. Repetto, "A data-driven approach to predict hourly load profiles from time-of-use electricity bills", in IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3286020. [Crossref]

For more details, please refer to the LICENSE.md file for the code license and the LICENSE_DATA.md file for the data license.

Corresponding Author: Gianmarco Lorenti [email protected].