Annual energy demand estimation of Iran industrial sector by Fuzzy regression and ARIMA

Author

Mehr, M.N. ; Samavati, Faramarz F. ; Jeihoonian, M.

Author_Institution

South Tehran Branch, Coll. of Ind. Eng., Islamic Azad Univ., Tehran, Iran

Volume

1

fYear

2011

fDate

26-28 July 2011

Firstpage

593

Lastpage

597

Abstract

This research presents a fuzzy regression model to efficiently estimate long term energy consumption in industry sector of Iran from 1982 to 2006. Four independent variables such as energy price, energy intensity, gross domestic production, and employment are introduced as model inputs. The presented model better estimates energy consumption than the conventional technique, auto regressive integrated moving average (ARIMA), based on the preprocessed provided data in terms of mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). In addition, the applicability and efficiency of the provided Fuzzy regression model is tested through analysis of variance (ANOVA) and proved its remarkable performance.

Keywords

autoregressive moving average processes; demand forecasting; energy consumption; fuzzy set theory; regression analysis; ANOVA; ARIMA model; Iran industrial sector; analysis of variance; annual energy demand estimation; autoregressive integrated moving average model; energy intensity; energy price; fuzzy regression model; gross domestic production; long term energy consumption estimation; mean absolute percentage error; root mean square error; Computational modeling; Data models; Energy consumption; Estimation; Forecasting; Mathematical model; Predictive models; ANOVA; ARIMA; Energy demand; Fuzzy regression; Long term energy consumption;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-61284-180-9

Type

conf

DOI

10.1109/FSKD.2011.6019565

Filename

6019565