@article {10.3844/ajeassp.2020.759.767,
article_type = {journal},
title = {Using Regression Analysis to Predict the Demand Function of Electricity: A Case Study},
author = {Khozani, Hasan Karimian and Esmaeili, Esmaeil and Bisheh, Mohammad Najjartabar and Ayatollahi, Seyed Ahmad and Gilanifar, Mostafa},
volume = {13},
number = {4},
year = {2020},
month = {Dec},
pages = {759-767},
doi = {10.3844/ajeassp.2020.759.767},
url = {https://thescipub.com/abstract/ajeassp.2020.759.767},
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},
journal = {American Journal of Engineering and Applied Sciences},
publisher = {Science Publications}
}