Ensemble Learning Based Electrical Load Prediction

Electrical load is a stochastic variable; hence its prediction can be a difficult task. The “curse of dimensionality” can also reduce a predictor's capacity for generalization. In contrast to choosing a subset of features that could lead to lost information, the ensemble system for load forecasting in this paper is based on random forests and uses all available features. Regression systems are built using various feature sets in the proposed method, and they are then combined using the average method. The system is trained on a large dataset using various machine learning algorithms and evaluated using various performance metrics. The results show that the proposed system is able to achieve high accuracy and outperform traditional forecasting methods. The developed system has the potential to improve the efficiency of power systems and enable better decision-making in the energy sector.