An integrated simulated-based fuzzy regression algorithm and time series for energy consumption estimation with non-stationary data and case studies

This study presents an integrated fuzzy regression, computer simulation and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional fuzzy regression or time series and the integrated algorithm could be an ideal substitute for such cases. After reviewing various fuzzy regression models and studying their advantages and shortcomings, the preferred model is selected for estimation by the proposed algorithm. Computer simulation is developed to generate random variables for monthly electricity consumption. Truly, fuzzy regression is run with Computer simulation output too. Preferred Time series model is selected from linear or nonlinear models. For this, after selecting preferred ARIMA model, Mcleod-Li test is applied to determine nonlinearity condition. When, nonlinearity condition is satisfied, the preferred nonlinear model is selected and defined as preferred time series model. At last, preferred model from fuzzy regression and time series model is selected by Granger-Newbold. Also, the impact of data preprocessing and post processing on the fuzzy regression performance is considered by the proposed algorithm. In addition, another unique feature of the proposed algorithm is utilization of Autocorrelation Function (ACF) to define input variables whereas conventional methods use trial and error method. Monthly electricity consumption of Iran from March 1994 to February 2005 is considered as the case of this study. The MAPE estimation of Genetic Algorithm (GA), Artificial Neural Network (ANN) versus the proposed algorithm shows the appropriateness of the proposed algorithm.