🚀 Live Demo on Hugging Face Spaces
A deep learning-based web application for predicting solar panel efficiency across India, featuring real data for 50+ cities.
- 🇮🇳 India Solar Map - Interactive visualization of solar potential across 50+ Indian cities
- 🔮 Efficiency Prediction - Real-time predictions based on environmental parameters
- 📊 Data Analysis - Comprehensive charts, correlations, and state-wise comparisons
- 🏆 City Rankings - Find the best locations for solar installations in any state
- Frontend: Streamlit
- Visualization: Plotly, Matplotlib
- Data Processing: Pandas, NumPy
- Deep Learning: TensorFlow/Keras (optional)
# Clone the repository
git clone https://github.com/aarush130/SolarPanelEfficiencyDL.git
cd SolarPanelEfficiencyDL
# Install dependencies
pip install -r requirements.txt
# Run the app
streamlit run app.py- Ministry of New and Renewable Energy (MNRE)
- India Meteorological Department (IMD)
- National Institute of Solar Energy (NISE)
| Rank | City | State | GHI (kWh/m²/day) |
|---|---|---|---|
| 1 | Leh | Ladakh | 5.90 |
| 2 | Jaisalmer | Rajasthan | 5.89 |
| 3 | Jodhpur | Rajasthan | 5.85 |
| 4 | Kutch | Gujarat | 5.82 |
| 5 | Bikaner | Rajasthan | 5.80 |
SolarPanelEfficiencyDL/
├── app.py # Main Streamlit application
├── requirements.txt # Python dependencies
├── src/
│ ├── data_generator.py # Synthetic data generation
│ ├── preprocessing.py # Data preprocessing
│ ├── model.py # Deep learning models
│ ├── train.py # Training pipeline
│ └── utils.py # Utility functions
├── notebooks/
│ └── exploration.ipynb # Data exploration notebook
└── README.md
Aarush Saxena
VIT University
Final Semester Project - B.Tech
MIT License - Feel free to use this project for learning and research.
Built with ❤️ using Streamlit & Plotly