A Comparative Analysis for Machine Learning-based Short-Term Load Forecasting Techniques

Short-term utility grid planning and operation are based on projected future energy consumption and transmission/generation capacity. The industrial sector being a major influence on the total electrical power consumption. Despite its importance, industrial load forecasting (LF) is not a widely discussed topic in literature, despite the fact that it is influenced by a number of factors, such as planned operations and work shifts, that are unusual or unnecessary in conventional load forecasting models. Unlike time-series analysis, machine learning (ML) techniques are well-suited to the nature of the loads since they can simulate complex nonlinear relationships through a process that uses previous data patterns. In this paper, four data-driven machine learning strategies including Support Vector Machine (SVM), Decision Tree (DT), Artificial Neural Network (ANN), and Gaussian Process (GP), are compared to forecast an industrial load. These strategies can help in selecting the optimal model for load prediction. The results show that GP outperformed other ML techniques, thus yielding more accurate and reliable readings with lower estimation errors for electrical LF, but at the expense of higher computational time.