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+# Energy Consumption Prediction
+
+## Description
+A machine learning model to predict energy consumption patterns for buildings, households, or industrial facilities. This project helps optimize energy usage and reduce costs through accurate forecasting.
+
+## Project Structure
+```
+Energy-Consumption-Prediction/
+├── data/                    # Dataset files
+├── notebooks/               # Jupyter notebooks
+├── src/                     # Source code
+├── models/                  # Saved models
+├── requirements.txt         # Dependencies
+└── README.md               # Project documentation
+```
+
+## Dataset
+The dataset includes energy consumption data with features such as:
+- Temporal features (hour, day, month, season)
+- Weather conditions (temperature, humidity, wind speed)
+- Building characteristics (size, type, occupancy)
+- Historical consumption patterns
+- Holiday and weekend indicators
+
+## Installation
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+```python
+from src.model import EnergyPredictor
+
+predictor = EnergyPredictor()
+predictor.load_model('models/energy_model.pkl')
+prediction = predictor.predict(input_features)
+```
+
+## Model Details
+- **Algorithm**: LSTM, XGBoost, Random Forest, Prophet
+- **Features**: 25+ engineered features including lag variables
+- **Metrics**: MAE, RMSE, MAPE, R-squared
+
+## Results
+| Model | MAE | RMSE | MAPE | R-squared |
+|-------|-----|------|------|----------|
+| LSTM | 45.2 | 62.3 | 8.5% | 0.92 |
+| XGBoost | 48.1 | 65.7 | 9.1% | 0.90 |
+| Random Forest | 51.3 | 68.9 | 9.8% | 0.88 |
+| Prophet | 52.8 | 71.2 | 10.2% | 0.86 |
+
+## Applications
+- Smart grid optimization
+- Building energy management
+- Cost forecasting for utilities
+- Demand response planning
+
+## Contributing
+Contributions are welcome! Please read the contributing guidelines before submitting a pull request.
+
+## License
+MIT License