Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/cnoret/hexa-watts
Interactive data visualization and machine learning app for energy consumption analysis and prediction in France, built with Streamlit. (Text in French)
https://github.com/cnoret/hexa-watts
data-visualization electricity-forecasting energy-analysis france machine-learning scikit-learn streamlit
Last synced: 1 day ago
JSON representation
Interactive data visualization and machine learning app for energy consumption analysis and prediction in France, built with Streamlit. (Text in French)
- Host: GitHub
- URL: https://github.com/cnoret/hexa-watts
- Owner: cnoret
- License: mit
- Created: 2023-05-26T05:28:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-28T23:12:53.000Z (13 days ago)
- Last Synced: 2025-01-29T00:23:59.447Z (13 days ago)
- Topics: data-visualization, electricity-forecasting, energy-analysis, france, machine-learning, scikit-learn, streamlit
- Language: Python
- Homepage: https://hexa-watts.streamlit.app/
- Size: 71.3 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Hexa Watts: Energy Consumption Prediction Application
## đ Overview
**Hexa Watts** is a Streamlit-based web application that allows users to analyze and predict electricity consumption across French regions. The app provides visualizations, exploratory data analysis (EDA), and machine learning predictions to support energy management and planning.
> **Note**: The application's interface and explanatory text are in French. This is to provide a localized experience for French-speaking users.
---
## đ¯ Features
1. **Data Visualization**:
- Regional energy production and consumption trends.
- Renewable energy production breakdown (solar, wind, hydro, bioenergy).
- European energy comparisons using Eurostat data.
2. **Machine Learning**:
- Predicts electricity consumption (in MW) based on region, date, time, and temperature data.
- Linear Regression model trained on historical data.
3. **Data Sources**:
- French energy consumption data: **RTE eco2mix**.
- Regional population statistics: **INSEE**.
- Regional temperature data: **MÊtÊo France**.
- European energy data: **Eurostat**.
---
## đ§ Installation
To run the application locally, follow these steps:
### 1. Clone the Repository
```bash
git clone https://github.com/your-username/energie-france.git
cd energie-france
```
### 2. Create a Virtual Environment
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
### 3. Install Dependencies
```bash
pip install -r requirements.txt
```
### 4. Run the Application
```bash
streamlit run streamlit_app.py
```
---
## đ Project Structure
```
energie-france/
âââ datasets/ # Data files used for analysis and predictions
âââ models/ # Pre-trained ML models (e.g., Linear Regression)
âââ content/ # Modular Streamlit pages (e.g., Introduction, Visualizations)
âââ images/ # Image assets for the application
âââ streamlit_app.py # Main application entry point
âââ requirements.txt # Dependencies for the project
âââ README.md # Project documentation
```
---
## đ Data Sources
1. [RTE Eco2Mix](https://odre.opendatasoft.com/explore/dataset/eco2mix-regional-cons-def)
2. [INSEE Statistics](https://www.insee.fr/fr/statistiques)
3. [MÊtÊo France](https://donneespubliques.meteofrance.fr)
4. [Eurostat](https://ec.europa.eu/eurostat)
---
## đ§Ŧ Technologies Used
- **Python**: Primary programming language.
- **Streamlit**: Framework for building the web application.
- **Pandas**: Data manipulation and analysis.
- **Plotly**: Interactive visualizations.
- **Scikit-Learn**: Machine learning for predictions.
- **Joblib**: Model serialization.
---
## đ License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.