Statistical Modeling of the Determinants Driving the Electricity Demand in Jordan

Abstract

The paper introduces a statistical model that connects the electrical demand in Jordan with several determinants that have a direct impact on the electrical consumption and load profile during the study period from 2007 to 2020. The period was selected as it is characterized by several global events that directly impacted Jordan’s economy and energy sustainability in Jordan, such as the Arab spring protests, the civil war in Syria, and the global financial crises. Many determinants that are used in the regression analysis imply the ambient temperature, day of the week, population, gross domestic product (GDP), oil price, and technological factors related to renewable energy projects. Results show that temperature and population positively impact the demand, whereas GPD, population, oil prices, and renewable energy negatively impact the electricity demand. The results obtained from backcasting regression analysis for the hourly 4745 data set covering 13 years period reveals reasonable error metrics with MAE, MAPE, and RMSE values of 134, 6.3% and 2.76%, respectively. The government must encourage investments to exploit and explore the massive potential of available energy resources such as oil, natural gas, oil shale, and uranium to resolve the problems related to the high global oil prices and high dependency on imported energy. Also, it is required to enable the transition from fossil fuels to renewable energy through financial incentives and tax exemption to encourage investments in clean energy, rebuild a new traffic system showing the volatile electricity prices, which are still unknown and finally remove obstacles and facilitate the ongoing projects, reaching a state of stakeholder buy-in engaging with the projects.