Selecting the appropriate scenario to predict the energy demand of residential and commercial sectors using Particle swarm optimization

doi:10.22055/jqe.2013.11404

Document Type : Research Paper

https://doi.org/10.22055/jqe.2013.11404

Abstract

Besides other production factors, energy has had a determinative role in economic growth of the countries and its importance increases in recent decades. The global economic growth and the industrialization process lead to an increase in energy demand and consumption. On the other hand, the residential and commercial sectors are the biggest energy consumption sectors, as it has allocated more than 34% of energy consumption amount to itself compared to other sectors. Therefore, for controlling the energy supply and demand and for correct planning, the energy consumption of these sectors must be predicted exactly. In this article, the future status of energy demand of residential and commercial sectors in Iran is predicted using variables affecting energy demand of these sectors. By using the 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 for model development and the appropriate scenario choice. 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 suitable model. Finally, energy demand of residential and commercial sectors is estimated up to the year 2032.

Keywords

References

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Quarterly Journal of Quantitative Economics

Selecting the appropriate scenario to predict the energy demand of residential and commercial sectors using Particle swarm optimization

References

References

Volume 10, Issue 3 - Serial Number 3
December 2013
Pages 1-19

Selecting the appropriate scenario to predict the energy demand of residential and commercial sectors using Particle swarm optimization

References

References

Volume 10, Issue 3 - Serial Number 3December 2013Pages 1-19

Volume 10, Issue 3 - Serial Number 3
December 2013
Pages 1-19