Coal demand estimating in Iran based on socio-economic indicators using particle swarm optimisation and genetic algorithm

Abstract

The main objective of this research is to investigate Iran's coal demand, projection and supplies by using the structure of the Iranian socio-economic conditions. This study develops a scenario to analyse coal consumption and make future projections based on particle swarm optimisation (PSO) and genetic algorithm (GA) methods. The models developed in two forms (exponential and linear) and applied to the coal demand of Iran. PSO and GA demand estimation models (PSO-DEM and GA-DEM) are developed to estimate the future coal demand values based on population, gross domestic product (GDP), import and export figures. Coal consumption in Iran from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, relative error averages were 2.121% and 10.647% for GA — DEMexponential and GA — DEMlinear and were 1.921% for 3.885% for PSO — DEMexponential and PSO — DEMlinear. Coal demand is forecasted up to year 2030.