AU - Sadeghi, Hossein
AU - Zolfaghari, Mahdi
AU - Heydarizade, Mohamad
TI - Estimation of Electricity Demand in Residential Sector Using Genetic Algorithm Approach
PT - JOURNAL ARTICLE
TA - IUST
JN - IUST
VO - 22
VI - 1
IP - 1
4099 - http://ijiepr.iust.ac.ir/article-1-273-en.html
4100 - http://ijiepr.iust.ac.ir/article-1-273-en.pdf
SO - IUST 1
AB - This paper aimed at estimation of the per capita consumption of electricity in residential sector based on economic indicators in Iran. The Genetic Algorithm Electricity Demand Model (GAEDM) was developed based on the past data using the genetic algorithm approach (GAA). The economic indicators used during the model development include: gross domestic product (GDP) in terms of per capita and real price of electricity and natural gas in residential sector. Three forms of GAEDM were developed to estimate the electricity demand. The developed models were validated with actual data, and the best estimated model was selected on base of evaluation criteria. The results showed that the exponential form had more precision to estimate the electricity demand than two other models. Finally, the future estimation of electricity demand was projected between 2009 and 2025 by three forms of the equations linear, quadratic and exponential under different scenarios .
CP - IRAN
IN -
LG - eng
PB - IUST
PG - 43
PT - Research
YR - 2011