A comprehensive web dashboard for analyzing and predicting electric vehicle (EV) charging patterns and their impact on the energy grid.
- ✨ Overview
- 💡 Why It Matters
- 🎯 Key Features
- 📊 Live Preview & Screenshots
- 🛠️ Tech Stack
- 🚀 Getting Started
- 🤝 Contributing
- 🙋♂️ About Me
- 📄 License
- 📬 Contact
Electro-Dash Insight (live at eco-amp.netlify.app) is a web based application designed to provide deep insights into electric vehicle charging behavior. The platform features an interactive frontend built with React that visualizes complex datasets, and it is powered by a backend with machine learning models that forecast energy consumption and charging demand.
This tool helps users, from grid operators to researchers, understand the dynamics of EV charging and plan for a more sustainable energy future.
As the world transitions to electric mobility, the strain on our electrical grids is becoming a critical challenge. Unmanaged EV charging can lead to power instability and costly infrastructure upgrades.
- Enables Smart Grids: By providing accurate forecasts, this tool allows utility companies to better manage energy loads, prevent outages, and integrate renewable energy sources more effectively. - Informs Policy & Planning: The data-driven insights help policymakers and urban planners design better charging infrastructure and incentive programs. - Empowers Consumers: Visualizing charging data can help EV owners understand their consumption patterns and make smarter, more economical charging decisions.
Electro-Dash Insight is more than a dashboard; it's a tool for building a more resilient and sustainable energy ecosystem for the electric revolution.
- Interactive Dashboard: A rich, responsive user interface built with React and styled with Tailwind CSS.
- Predictive Analytics: Leverages Python and ML models to forecast future charging demand and energy consumption.
- Data Visualization: Clear and insightful charts and graphs to represent complex energy data.
- Full-Stack Integration: Seamless communication between the React frontend and the backend API.
- Live Deployment: Fully functional and publicly accessible via Netlify.
You can access the live application here: https://eco-amp.netlify.app
Below are screenshots of the main dashboard and the ML model analytics view.
Live Webpage Preview:
ML Model Dashboard:
This project is built with a modern, full-stack technology set:
To run this project locally, follow these steps.
- Node.js and npm (or yarn) - Python 3.9+ and pip
1. Clone the repository:
bash git clone [https://github.com/sharvesh1401/electro-dash-insight.git](https://github.com/sharvesh1401/electro-dash-insight.git) cd electro-dash-insight
2. Install frontend dependencies:
bash # Navigate to the frontend directory if it's separate npm install
3. Set up the backend (instructions for a separate backend folder):
bash cd ../backend # Example pip install -r requirements.txt
4. Run the application: ```bash # Run the frontend npm run dev
# Run the backend server in a separate terminal python app.py ```
Contributions make the open-source community an amazing place to learn, create, and inspire. Any contributions you make are greatly appreciated.
1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/NewFeature)
3. Commit your Changes (git commit -m 'Add some NewFeature')
4. Push to the Branch (git push origin feature/NewFeature)
5. Open a Pull Request
I'm Sharvesh Selvakumar, an engineering student passionate about AI, clean energy, and responsible tech.
🔗 Portfolio: sharveshfolio.netlify.app 🐦 Twitter: @Sharvesh_14
This project is distributed under the MIT License. See the LICENSE file for more information.
Sharvesh Selvakumar - s_sharvesh@outlook.com
Project Link: https://github.com/sharvesh1401/electro-dash-insight