Estimation of Electricity Demand in Residential Sector Using Genetic Algorithm Approach

Publish Year: 1390
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:

JR_IJIEPR-22-1_005

تاریخ نمایه سازی: 7 شهریور 1393

Abstract:

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.

Authors

Hossein Sadeghi

Assistance professor, Tarbiat Modares University, Tehran, Iran

Mahdi Zolfaghari

PhD student, Tarbiat Modares University, Tehran, Iran

Mohamad Heydarizade

MSc of electricity restructure - Power and water University of technology