A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid

Abstract: With the rapid growth of the world's population, global electricity demand has skyrocketed. As a result, effective energy management mechanisms are required. Because energy consumption trends are rather volatile. To develop the optimization and control mechanism, precise energy demand estimation and short and/or long-term forecasting results with higher accuracy are required. As a result, machine learning (ML) techniques, in conjunction with distributed demand response programs, are being used to accurately predict future energy demand requirements. In this paper, the performance of various state-of-the-art ML algorithms such as logistic regression (LR), support vector machines (SVM), naive Bayes (NB), decision tree classifier (DTC), K-nearest neighbor (KNN), CatBoost and Extra Tree is examined. The primary goal of this paper is to present a comparison of machine learning (ML) algorithms for short-term load forecasting (STLF) in terms of accuracy and forecast error. Based on the implementation and analysis, we discovered that, when compared to other algorithms, the DTC produces comparatively better results