Title :
Prediction of Iran´s annual electricity demand: Artificial intelligence approaches
Author :
Homayoun Hamed Moghadam Rafati;Mahdi Jalili;Hamed Davari;Reza Maknoon
Author_Institution :
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
Abstract :
Accurate prediction of electricity demand is essential for planning, policy making and resource allocation in national level. In this manuscript, we applied a number of artificial intelligence methods to predict macro-scale electricity consumption rates in Iran. To this end, three socio-economic and three environmental factors were considered as inputs to the prediction models. We used data for the period 1967-2013 in order to predict the power demand in the years 2014-2018. Experimental results showed that the path coefficient analysis model with linear coefficients had the best performance among the models considered in this study. The outcome of this research can help the policy makers to better understand the mark needs.
Keywords :
"Biological system modeling","Sociology","Predictive models","Mathematical model","Training","Principal component analysis"
Conference_Titel :
Innovations in Information Technology (IIT), 2015 11th International Conference on
Print_ISBN :
978-1-4673-8509-1
DOI :
10.1109/INNOVATIONS.2015.7381570