A Data-Driven System to Forecast Electric Vehicle (EV) Charging Demand Across Washington State
AICTE Shell-Edunet Skills4Future Internship Project
Electric Vehicles (EVs) are revolutionizing transportation, but efficient charging infrastructure is essential for sustainable adoption. This project leverages historical EV registration data to build a predictive model for forecasting adoption trends across Washington State counties.
- County-Level Forecasting: Predict EV adoption for any Washington State county
- Interactive Dashboard: Beautiful Streamlit interface with dark theme
- 3-Year Projections: Visualize growth trends with historical context
- Multi-County Comparison: Analyze regional adoption patterns
- Machine Learning Model: RandomForest-based forecasting engine
| Component | Technology |
|---|---|
| Core Language | Python 3.10 |
| Data Processing | pandas, numpy |
| Visualization | matplotlib, Plotly |
| ML Framework | scikit-learn (RandomForestRegressor) |
| Web Framework | Streamlit |
| Deployment | Render (via Procfile) |
EV-vehicle-demand-prediction/
├── assets/
│ ├── car.png
│ └── ev-car-factory.jpg
├── data/
│ ├── EV_Population_By_County.csv
│ └── preprocessed_ev_data.csv
├── notebook/
│ └── EV_DemandPrediction.ipynb
├── app.py
├── forecasting_ev_model.pkl
├── requirements.txt
├── runtime.txt
├── Procfile
├── LICENSE
└── README.md
Deployed live on Render: https://ev-demand-forecast.onrender.com
Follow these instructions to set up the project locally.
git clone https://github.com/XynaxDev/EV-vehicle-demand-prediction.git
cd EV-vehicle-demand-prediction
pip install -r requirements.txt
streamlit run app.pyThis project is licensed under the MIT License.
- AICTE & Shell Edunet Skills4Future Internship Program
- Inspired by best practices from real-world EV infrastructure projects.
Made with 💌 and Streamlit by Akash | © 2025 AICTE Internship Project