DocumentCode
723727
Title
Analysis and predicting electricity energy consumption using data mining techniques — A case study I.R. Iran — Mazandaran province
Author
Karimtabar, Noorollah ; Pasban, Sadegh ; Alipour, Siavash
Author_Institution
Minist. of Educ., Babol, Iran
fYear
2015
fDate
11-12 March 2015
Firstpage
1
Lastpage
6
Abstract
The electricity consumption forecast is especially important with regard to policy making in developing countries. In this paper, the electricity consumption rate is predicted using the data mining techniques. The datasets that were collected for predicting the electricity consumption are related to Islamic Republic of Iran - Mazandaran province pertaining to the years 1991 to 2013. The research objective is analyzing the electricity consumption rate in recent years and predicting future consumption. According to a study the electricity consumption growth rate between the years 2006 to 2013 and the years 1999 to 2006 equaled 28.41 and 73.53, respectively. The results of the research conducted using the regression model indicate a 2.48 relative error. The output of this prediction shows that the total electricity consumption rate increases about 3.2% annually on average and will reach 7076796 megawatts by the year 2020 that shows a 22.28% growth comparing to the year 2013.
Keywords
data mining; energy consumption; forecasting theory; power engineering computing; regression analysis; Islamic Republic of Iran; Mazandaran province; data mining techniques; electricity consumption forecasting; electricity consumption rate prediction; electricity energy consumption; policy making; regression model; total electricity consumption rate; Energy consumption; Moisture; Numerical models; Power demand; Predictive models; Sociology; Statistics; Data-mining; dataset; electricity consumption rate; prediction; relative error;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International Conference on
Conference_Location
Rasht
Print_ISBN
978-1-4799-8444-2
Type
conf
DOI
10.1109/PRIA.2015.7161634
Filename
7161634
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