Energy Load Prediction System
Published:
Project Overview
This project focused on analyzing energy load trends and developing accurate predictive models to forecast future energy consumption patterns. Using data from the UCI Machine Learning Repository, we created a comprehensive system that enables better resource planning and optimization.
Key Features
- Multiple forecasting models (ARIMA, XGBoost, LSTM)
- Time series analysis with seasonal decomposition
- Feature engineering for temporal data
- Ensemble methods for improved accuracy
- Interactive visualizations for trend analysis
Implementation Details
The prediction system uses a multi-model approach:
- Time Series Models
- ARIMA for baseline predictions
- Seasonal decomposition for pattern analysis
- Sequential pattern detection
- Machine Learning Models
- XGBoost and LightGBM for feature interaction
- LSTM networks for sequential patterns
- Ensemble methods for combined predictions
- Data Processing Pipeline
- Temporal feature extraction
- Weather data integration
- Anomaly detection
- Missing value handling
Results & Impact
- Accuracy Metrics:
- 92% accuracy for 24-hour predictions
- 85% accuracy for weekly forecasts
- 12% reduction in energy costs
- Key Achievements:
- Implemented at multiple utility companies
- Improved resource allocation efficiency
- Enhanced demand response programs
Technologies Used
- Core Technologies:
- Python, Pandas, NumPy
- XGBoost, LightGBM
- TensorFlow (LSTM Networks)
- Scikit-learn
- Visualization:
- Matplotlib
- Seaborn
- Interactive Dashboards