Paper Information

Journal:   IRANIAN JOURNAL OF MANAGEMENT STUDIES   JANUARY 2016 , Volume 9 , Number 1; Page(s) 101 To 123.
 
Paper: 

SELECTING THE APPROPRIATE SCENARIO FOR FORECASTING ENERGY DEMANDS OF RESIDENTIAL AND COMMERCIAL SECTORS IN IRAN USING TWO METAHEURISTIC ALGORITHMS

 
 
Author(s):  NAZARI HESAM, KAZEMI ALIYEH*, HASHEMI MOHAMMAD HOSEIN
 
* FACULTY OF MANAGEMENT, UNIVERSITY OF TEHRAN, TEHRAN, IRAN
 
Abstract: 

This study focuses on the forecasting of energy demands of residential and commercial sectors using linear and exponential functions. The coefficients were obtained from genetic and particle swarm optimization (PSO) algorithms. Totally, 72 different scenarios with various inputs were investigated. Consumption data in respect of residential and commercial sectors in Iran were collected from the annual reports of the central bank, Ministry of Energy and the Petroleum Ministry of Iran (2010). The data from 1967 to 2010 were considered for the case of this study. The available data were used partly to obtain the optimal, or near optimal values of the coefficient parameters (1967–2006) and for testing the models (2007–2010). Results show that the PSO energy demand estimation exponential model with inputs, including value added of all economic sectors, value of made buildings, population, and price indices of electrical and fuel appliances using the mean absolute percentage error on tests data were 1.97%, was considered the most suitable model. Finally, basing on the best scenario, the energy demand of residential and commercial sectors is estimated at 1718 mega barrels of oil equivalent up to the year 2032.

 
Keyword(s): ENERGY DEMAND, FORECASTING, GENETIC ALGORITHM, PARTICLE SWARM OPTIMIZATION ALGORITHM, RESIDENTIAL AND COMMERCIAL SECTORS
 
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