README.md

metadata

title: Electricity Consumption Predictor
emoji: ⚡
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.39.0
app_file: app.py
pinned: false
license: mit
tags:
  - machine-learning
  - regression
  - electricity
  - consumption
  - prediction
  - gradio
datasets:
  - synthetic-electricity-data
metrics:
  - mse
  - rmse
  - mae
  - r2

⚡ 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. Clone the repository:

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

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   python app.py
   

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

Temperature	Day	Event	Predicted Consumption
25°C	Monday	None	16.5 kWh
35°C	Saturday	Holiday	22.3 kWh
15°C	Wednesday	None	14.1 kWh

🔧 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. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
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

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Built with ❤️ using Gradio and scikit-learn