Selecting The Appropriate Scenario to Predict the Energy Demand of Residential and Commercial Sectors using Particle Swarm Optimization

Energy has had a determinative role in economic growth of countries and its importance has been increasing during recent decades. The global economic growth and the industrialization process is one of the main sources of increasing demand and consumption of energy. On the other hand, the residential and commercial sectors are the largest energy consumption sectors, i.e. 34% of energy consumption compared to other sectors. Thus, to plan and control the amount of energy supply and demand, predicting energy consumption of these sectors would be helpful for policy makers. In this paper, the future status of energy demand of residential and commercial sectors in Iran is predicted using variables affecting energy demand of these sectors. Using PSO algorithm, both linear and exponential forms of energy demand equations were studied under 54 different scenarios with various variables. The data from 1968 to 2011 were applied to develop model and to choose the appropriate scenario. Results show that an exponential model with inputs including total value added minus that of the oil sector, value of made buildings, total number of households and consumer energy price index is the most appropriate model. Furthermore, energy demand of residential and commercial sectors is estimated for following years up to 2032.