Modeling and Forecasting the Trend of Electricity Consumption in Iran

Document Type : Original Article

Authors

1 . M.A. in Economic Systems Planning, Shahrood University of Technology, Shahrood, Iran

2 Assistant Professor, Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran

3 M.A. in Economic Systems Planning, Shahrood University of Technology, Shahrood, Iran

10.22059/ses.2024.377117.1081

Abstract

Accurate forecasting of a country's energy consumption trend is crucial in addressing the imbalance between supply and demand. Given the significant contribution of electricity to Iran's energy consumption portfolio, this research aims to model and forecast the trend of electricity consumption in Iran. Factors influencing the trend of electricity consumption were identified based on previous studies, and relevant data were collected for the period 1978-2021develop forecast models. Various models and methods were employed to predict the trend of electricity consumption, including simple indicators, energy consumption intensity, trend line analysis, regression modeling, and neural networks. The regression model estimation results indicate that the trend of electricity consumption in Iran is significantly influenced by per capita income and consumption in the previous period. From a statistical perspective, other variables such as energy price, air temperature, and rainfall did not have a significant impact on the trend. The results show that electricity consumption in Iran has increased by approximately 22.14% over the period 1978-2021, with an average annual growth rate of 7.49%. According to the forecast, electricity consumption is expected to reach 455,603 thousand megawatts by 2026. In contrast, the regression model forecast for this year is 368,959 thousand megawatts. A comparison of the prediction results reveals that the accuracy of different models and approaches varies, with the regression method exhibiting a lower measurement error than the other investigated methods in predicting the electricity consumption trend.

Keywords

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References

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Journal of sustainable Energy Systems
Volume 3, Issue 3
July 2024
Pages 323-339

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