Electric Load Forecasting Using Machine Learning
Published:
Overview
Accurate electric load forecasting is essential for reliable and economical power system operation. This project investigates short-term electrical load prediction using statistical methods and machine learning algorithms. The objective is to develop forecasting models capable of capturing temporal demand patterns while improving prediction accuracy for power system planning and operational decision-making.
Objectives
- Develop forecasting models for short-term electrical load prediction.
- Compare classical statistical approaches with machine learning methods.
- Evaluate forecasting accuracy using standard performance metrics.
- Investigate the effects of weather and calendar variables on electricity demand.
Methods
The project follows a typical forecasting workflow:
- Data preprocessing and cleaning
- Feature engineering
- Time-series analysis
- Model training and validation
- Forecast evaluation
Potential forecasting models include:
- Linear Regression
- ARIMA
- Random Forest
- Gradient Boosting
- XGBoost
- Long Short-Term Memory (LSTM) neural networks
Data Sources
Potential datasets include:
- Public utility load data
- Regional transmission operator (RTO/ISO) datasets
- Weather observations
- Calendar information (weekday, holidays, seasons)
Evaluation Metrics
Model performance is evaluated using commonly accepted forecasting metrics:
- Mean Absolute Error (MAE)
- Root Mean Square Error (RMSE)
- Mean Absolute Percentage Error (MAPE)
- Coefficient of Determination (R²)
Expected Outcomes
The project aims to identify forecasting models that provide improved prediction accuracy while remaining computationally efficient for practical deployment in modern power systems.
Skills Demonstrated
- Time-series analysis
- Statistical modeling
- Machine learning
- Python
- Data visualization
- Feature engineering
- Model evaluation
- Power system analytics
Research Relevance
Load forecasting is a fundamental component of modern smart grids and supports:
- Economic dispatch
- Unit commitment
- Renewable energy integration
- Demand response
- Distribution system operation
- Energy management systems
Future Work
Future extensions of this work include:
- Probabilistic load forecasting
- Deep learning-based forecasting
- Transformer-based sequence models
- Graph neural networks for spatial load prediction
- Federated learning for privacy-preserving forecasting
- Explainable AI (XAI) techniques for forecasting models