Machine Learning Algorithms for Load Forecasting in Smart Grid

The smart grid, essential for modern power distribution, merges innovative technologies with traditional infrastructure to enhance efficiency and sustainability. As the global population grows, so does the demand for electricity, underscoring the importance of accurate load forecasting (LF) for smart grids’ reliability. Effective energy management is crucial, with LF techniques guiding power operations and upgrades. The emergence of machine learning (ML) algorithms in smart grid applications transforms energy management and grid operations. This study explores ML algorithms’ application in load forecasting, focusing on techniques such as artificial neural networks (ANN), support vector machine (SVM), random forest (RF), decision tree (DT), auto regressive integrated moving average (ARIMA), linear regression (LR), long short-term memory (LSTM), and convolutional neural networks (CNN). The paper provides detailed study on ANN’s adaptability, SVM’s high accuracy, and the efficiency of regression, random forests, and decision tree in short-term forecasting. The study positions ML algorithms as critical for the evolving smart grid landscape, ensuring reliable and efficient power services by providing precise and adaptive load forecasting in dynamic contexts.