Annual energy demand estimation of Iran industrial sector by Fuzzy regression and ARIMA
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.
Fuzzy regression Long term energy consumption ARIMA ANOVA Energy demand
M. Nikouei Mehr F. Famil Samavati M. Jeihoonian
College of Industrial engineering Islamic Azad University-South Tehran Branch Tehran, Iran Department of Industrial engineering Payame nor university Hamedan, Iran Department of Industrial Engineering, College of Engineering University of Tehran Tehran, Iran
国际会议
上海
英文
612-616
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）