energy-analysis-toolbox 0.1.2

 

energy_analysis_toolbox is a Python library designed to analyze and model power and energy time series. It provides a wide range of tools for processing time series, generating synthetic datasets, analyzing weather and thermosensitivity.

Features

• Time Series Resampling and Feature Extraction: Includes utilities for manipulating and resampling energy-related time series data, making it easy to work with data from different sources.
• Weather and Thermosensitivity Analysis: Tools for calculating degree days and assessing thermosensitivity.
• Power Consumption Analysis: Detection of unusual power consumption patterns, and analyze load profiles.
• Synthetic Data: Generation of synthetic time series energy datasets.
• Integration with Pandas: Many utilities integrate seamlessly with pandas DataFrames, allowing to extend pandas with energy and power-specific tools through an accessor.

Installation

energy_analysis_toolbox can be installed from the repository or via pip. Ensure that you use Python 3.10 or above.

pip install energy-analysis-toolbox

Alternatively, you can clone this repository and install the dependencies directly:

git clone https://github.com/Eco-CO2/energy_analysis_toolbox
cd energy_analysis_toolbox
pip install .

Usage

The toolbox is structured into several modules, each handling a different aspect of energy data processing. Below is a quick example to get started.

Example: Resampling a time series and computing energy

import numpy as np
import pandas as pd
import energy_analysis_toolbox.pandas
import matplotlib.pyplot as plt

power = pd.Series(
    data=5*np.sin(np.linspace(0, 6, 100)) + np.random.randn(100) + 7,
    index=pd.date_range(start='2023-01-01', periods=100, freq='d'),
)
energy_resampled = power.eat.to_energy().eat.to_freq("1W")/3600000

fig, axes = plt.subplots(1, 2, figsize=(8, 3))
power.plot(ax=axes[0], ylabel="Power (W)", title="Power")
energy_resampled.plot(ax=axes[1], ylabel="Energy (kWh)", title="Weekly resampled energy")

Documentation

The complete documentation, including detailed guides for each module, API reference, and tutorials, can be found here.

To generate the documentation locally, you can run:

./generate_doc.sh

Tests

The Energy Analysis Toolbox comes with a comprehensive test suite that ensures code reliability and robustness. The tests cover a variety of scenarios including time series resampling, power overconsumption analysis, and synthetic data generation.

To run the tests, execute the following command:

./run_tests.sh

We use pytest for unit testing, which helps ensure that our code is reliable and that any modifications do not break existing functionality.

Contributing

This toolbox was originally developed by the R&D team of Eco CO2. It is now open to anyone who wants to contribute: whether it is a bug fix, a new feature, or improving documentation, we appreciate your help! To contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to your branch (git push origin feature-branch)
5. Create a pull request

Please make sure to add appropriate unit tests for any new code and verify all tests pass.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For questions, feature requests, or to report issues, please open an issue on the GitHub issue tracker.