Machine Learning Approach For Short-Term Load Forecasting In Smart Grids

Effective operation and planning of power systems largely hinges on accurate load forecasting. This study delves into the capabilities of Machine Learning (ML) algorithms for enhancing the precision of Short-Term Load Forecasting (STLF). The research employs several ML models, namely Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosted Machine (LightGBM). These models leverage a variety of input features encompassing historical load data, meteorological data, and calendar data to predict future loads. The performance of these ML models is evaluated against that of a conventional load forecasting method, the Autoregressive Integrated Moving Average (ARIMA) model. The comparative analysis reveals that the ML models proposed in this study deliver superior results, thereby suggesting their potential for more effective power system operation and planning.