Forecasting Electricity Peak Load : Time-Series Modeling Integrating Economic and Demographic Dynamics—A Case Study From Jordan

Abstract

Factors like pricing, transmission expansion, and capacity planning rely on accurate power demand forecasts. This paper intends to utilize time-series models to forecast the peak electricity demand of Jordan's power grid amidst its energy transition, offering insights into necessary expansion and system adjustments over the next decade It explores the relationship between the country's peak load fluctuations over the last three decades and examining factors including the Gross Domestic Product (GDP) and population growth. Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Integrated Moving Average with Explanatory Variable (ARIMA-X), are employed to forecast yearly peak loads, which are also compared with linear regression, providing an enhanced understanding of power generation and network expansion needs for the coming decade. The results show strong correlations between peak load, population growth, and GDP, with the models proving effective in forecasting future peak loads, albeit with caution regarding ARIMA-X. Projections suggest a potential 41% increase in peak load by 2035, reaching around 5300 MW in 14 years. Assuming consistent growth rates in population and GDP, the projections of the peak load also indicate that the peak load might reach twice its current level in the next 2 to 2.5 decades.