A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach

Abstract

Time series modeling is an effective approach for studying and analyzing the future performance of the power sector based on historical data. This study proposes a forecasting framework that applies a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast the long-term performance of the electricity sector (electricity consumption, generation, peak load, and installed capacity). In this study, the model was used to forecast the aforementioned factors in Saudi Arabia for 30 years from 2021 to 2050. The historical data that were inputted into the model were collected from Saudi Arabia at quarterly intervals across a 40-year period (1980−2020). The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, which helps reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. The experimental findings indicated that the SARIMAX model has promising performance in terms of categorization and consideration, as it has significantly improved forecasting accuracy compared with the simpler autoregressive integrated moving average-based techniques. Furthermore, the model is capable of coping with different-sized sequential datasets. Finally, the model aims to help address the issue of a lack of future planning and analyses of power performance and intermittency, and it provides a reliable forecasting technique, which is a prerequisite for modern energy systems.