Comparative Analysis of Load Forecasting Algorithms Using Machine Learning

Load forecasting is a critical aspect of energy management, enabling utilities to optimize resource allocation and ensure grid stability and reliable power supply. This study proposes a comprehensive approach to enhancing load forecasting accuracy by combining Autoregressive Integrated Moving Average (ARIMA) with Seasonal and Exogenous factors known as Regressor Integrated Moving Average with exogenous factors (SARIMAX) which is categorized under statistical machine learning modeling techniques. The integration of exogenous variables, such as weather conditions and socio-economic factors, enhances the forecasting accuracy by capturing external influences on load demand. The ARIMA/SARIMAX hybrid model is trained on historical load data, exogenous variables, and seasonality patterns, providing a robust framework for capturing complex temporal dependencies. The study evaluates the proposed approach using real-world load datasets, comparing its performance against traditional ARIMA and machine learning models. The results demonstrate superior forecasting accuracy, illustrating the effectiveness of integrating exogenous factors into the ARIMA/SARIMAX model for load forecasting applications. This hybrid approach contributes to the advancement of reliable and precise load forecasting techniques, crucial for effective energy planning and management in modern power systems.