Research Article Open Access

Using Regression Analysis to Predict the Demand Function of Electricity: A Case Study

Hasan Karimian Khozani1, Esmaeil Esmaeili1, Mohammad Najjartabar Bisheh2, Seyed Ahmad Ayatollahi1 and Mostafa Gilanifar2
  • 1 Amirkabir University of Technology, Iran
  • 2 Kansas State University, United States

Abstract

Due to the growing electricity consumption in Iran, investigating the changes of the electricity demand is one of the fundamental challenges facing many professionals and planners. The planners always invest efforts to address this issue by accurately predicting the electricity demand over the years and increasing the extra capacity respectively. One of the main tools for predicting the electricity demand is a regression model. Generally, in the papers, to estimate the annual electricity demand, the electricity prices and GDP per capita have been considered as independent variables. In this study, we used the data pertinent to the electricity prices, GDP per capita and investment per capita from 1974 to 2007, to estimate the annual electricity demand. In our estimated model, price elasticity, income elasticity and investment elasticity were 0.187, -0.566 and 1.207 respectively. The annual demand for the electricity for years 2008 and 2009 was predicted. The low error rate between the actual values and the predicted values shows that this model is an acceptable model

American Journal of Engineering and Applied Sciences
Volume 13 No. 4, 2020, 759-767

DOI: https://doi.org/10.3844/ajeassp.2020.759.767

Submitted On: 7 October 2020 Published On: 20 December 2020

How to Cite: Khozani, H. K., Esmaeili, E., Bisheh, M. N., Ayatollahi, S. A. & Gilanifar, M. (2020). Using Regression Analysis to Predict the Demand Function of Electricity: A Case Study. American Journal of Engineering and Applied Sciences, 13(4), 759-767. https://doi.org/10.3844/ajeassp.2020.759.767

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Keywords

  • Demand Function
  • Regression
  • Electricity
  • Income Elasticity
  • Price Elasticity