A Comprehensive Study of Random Forest for Short-Term Load Forecasting

Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize. In this study, we use RF for short-term load forecasting (STLF), focusing on data representation and training modes. We consider seven methods of defining input patterns and three training modes: local, global and extended global. We also investigate key RF hyperparameters to learn about their optimal settings. The experimental part of the work demonstrates on four STLF problems that our model, in its optimal variant, can outperform both statistical and ML models, providing the most accurate forecasts.

Automated summary

The study focuses on using random forest (RF) for short-term load forecasting (STLF), with a focus on data representation and training modes. Experimental results show that the optimal RF model performs well on four STLF problems, outperforming statistical and machine learning models in accuracy.