A total fuzzy regression algorithm for energy consumption estimation

This study presents an integrated fuzzy regression and time series algorithm to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional time series and fuzzy regression could be an ideal substitute for such cases. After reviewing sixteen most important fuzzy regression model and studying their advantages, the best model is selected for estimation. In addition, another unique feature of this study is utilization of Autocorrelation Function (ACT) to define input variables versus trial and error method. Monthly electricity consumption of Iran from 1992 to 2004 is considered as the case of this study.