An integrated simulation-based fuzzy regression-time series algorithm for electricity consumption estimation with non-stationary data

This study presents an integrated fuzzy regression, computer simulation, 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 with non-stationary data. Since, it is difficult to model the uncertain behavior of energy consumption with only conventional fuzzy regression or time series, the integrated algorithm could be an ideal method for such cases. Computer simulation is developed to generate random variables for monthly electricity consumption. The fuzzy regression is run with computer simulation output too. A Granger-Newbold test is used to select the optimum model, which could be a time series, a fuzzy regression (with or without pre-processed data, PD) or a simulation-based fuzzy regression (with or without PD). The preferred time series model is selected from linear or nonlinear models. At last, the preferred model from fuzzy regression and time series models is selected by Granger-Newbold. Monthly electricity consumption in Iran from 1995 to 2005 is considered as the basis of this study. The mean absolute percentage error estimates of a genetic algorithm, an artificial neural network, and a fuzzy inference system versus the proposed algorithm show the appropriateness of the proposed algorithm. This is the first study that introduces an integrated simulation-based fuzzy regression-time series for electricity consumption estimation with an imprecise set of data.