surahj/electricity-consumption-predictor

⚡ Electricity Consumption Predictor

A machine learning application that predicts daily electricity consumption based on various factors like temperature, day of the week, and special events.

🚀 Live Demo


📊 Features

• —Temperature-based predictions: Considers how temperature affects electricity usage
• —Day of week analysis: Accounts for different consumption patterns on weekdays vs weekends
• —Special events: Factors in holidays and major events
• —Interactive interface: User-friendly Gradio web interface
• —Model insights: Detailed explanation of prediction factors

🛠️ Technology Stack

• —Machine Learning: scikit-learn (Linear Regression)
• —Data Processing: pandas, numpy
• —Web Interface: Gradio
• —Model Persistence: joblib

🏗️ Project Structure

├── src/
│   ├── app.py              # Main Gradio application
│   ├── model.py            # ML model implementation
│   └── data_generator.py   # Synthetic data generation
├── tests/
│   ├── test_model.py       # Model unit tests
│   ├── test_app.py         # App unit tests
│   └── test_integration.py # Integration tests
├── app.py                  # Hugging Face Spaces entry point
├── requirements.txt        # Python dependencies
└── README.md              # This file

🧪 Usage

Local Development

1. 1.Clone the repository:

   git clone https://github.com/YOUR_USERNAME/electricity-consumption-predictor.git
   cd electricity-consumption-predictor

1. 1.Install dependencies:

   pip install -r requirements.txt

1. 1.Run the application:

   python app.py

1. 1.Run tests:

   pytest tests/

Hugging Face Spaces

The app is automatically deployed on Hugging Face Spaces. Simply visit the live demo link above to use the application.

📈 How It Works

1. 1.Data Generation: Creates synthetic electricity consumption data with realistic patterns
2. 2.Model Training: Trains a linear regression model on historical data
3. 3.Feature Engineering: Extracts relevant features (temperature, day of week, events)
4. 4.Prediction: Uses the trained model to predict consumption for new scenarios
5. 5.Interpretation: Provides detailed breakdown of prediction factors

🎯 Model Features

• —Temperature Effect: Higher temperatures increase AC usage
• —Day of Week: Weekends typically have different consumption patterns
• —Base Consumption: Minimum daily electricity usage
• —Event Impact: Special events can significantly affect consumption

📊 Example Predictions

🔧 Configuration

The model can be customized by modifying parameters in src/model.py:

• —Training data size
• —Feature weights
• —Model hyperparameters

🧪 Testing

Run the test suite to ensure everything works correctly:

# Run all tests
pytest tests/

# Run with coverage
pytest tests/ --cov=src

# Run specific test file
pytest tests/test_model.py

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

1. 1.Fork the repository
2. 2.Create a feature branch
3. 3.Make your changes
4. 4.Add tests for new functionality
5. 5.Submit a pull request

📞 Support

If you encounter any issues or have questions:

• —Open an issue on GitHub
• —Check the Hugging Face Spaces discussion
• —Review the test files for usage examples

Built with ❤️ using Gradio and scikit-learn