M2-Enedis

Application Status

Status	Description
Current Version	1.0.0
Python Version	3.11

Overview

M2-Enedis is a dashboard application built using Dash and Plotly. It provides various functionalities including data visualization, machine learning predictions, and interactive maps. The application is designed to help users understand and analyze energy consumption data.

You can see an explanation of the application in the following video: Watch Video

Before install

Ensure you have the latest data in the dataset folder. If the repository does not contain the latest version, you can download it from the following URL: Download Latest Dataset

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/greentech_dashboard.git
   cd greentech_dashboard

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Running the Application

1. Start the Dash application:

   python app.py

2. Open your web browser and navigate to:

   http://127.0.0.1:8050/
   

Running API

1. Run API

    uvicorn main:app --reload

Example API Request

Api predict consumption

To send a request to the API, you can use the following example with curl:

curl -X POST "http://127.0.0.1:8000/predict_consomation" -H "Content-Type: application/json" -d '{
  "etiquette_dpe": 3.0,
  "type_batiment": 0.0,
  "annee_construction": 1921.0,
  "classe_inertie_batiment": 1.0,
  "hauteur_sous_plafond": 3.1,
  "surface_habitable_logement": 50.2,
  "type_energie_principale_chauffage": 11.0,
  "isolation_toiture": 1.0,
  "code_postal_ban": 69002.0
}'

Api predict label

curl -X POST "http://127.0.0.1:8000/predict_label" -H "Content-Type: application/json" -d '{
    "annee_construction": 1948,
    "surface_habitable_logement": 197.5,
    "cout_total_5_usages": 4415.2,
    "cout_ecs": 409,
    "cout_chauffage": 3937.8,
    "cout_eclairage": 60.4,
    "cout_auxiliaires": 5.0,
    "cout_refroidissement": 0.0
}'

Replace "http://127.0.0.1:8000/predict_etiquets" with the actual endpoint of your API.

Usage

• Home Page: Provides an introduction of our solution.
• Context Page: Provides an introduction of data and key performance indicators (KPIs).
• Map Page: Shows interactive maps for data visualization.
• Analytiques: Allows users to visualize energy consumption data through interactive graphs for deeper insights and trend analysis.
• Prediction Page: Allows users to make predictions using the trained machine learning model.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contributing

Nom	GitHub Profile
SOURAYA AHMED ABDEREMANE	Sahm269
BERTRAND KLEIN	bertrandklein
JUAN DIEGO ALFONSO OCAMPO	jdalfons
PIERRE BOURBON	pbrbn

Contact

For any questions or inquiries, please contact Juan Diego A. at jalfonsooc@univ-lyon2.fr.