A unique meta-heuristic algorithm for optimization of electricity consumption in energy-intensive industries with stochastic inputs

This study presents an integrated meta-heuristic algorithm for forecasting electricity consumption in energy-intensive industries with stochastic inputs. The algorithm is based on genetic algorithm (GA), conventional regression and analysis of variance (ANOVA). The economic indicators used in this paper are price, value added, number of customers and electricity consumption in the last periods. The proposed algorithm uses ANOVA to select either GA or conventional regression for future demand estimation. Furthermore, if the null hypothesis in ANOVA is rejected, Duncan Multiple Range Test is used to identify which model is closer to actual data at α level of significance. To show the applicability and superiority of the proposed algorithm, the data for electricity consumption in energy-intensive industries of Iran from 1979 to 2009 (in two cases) is used.