Electrical energy consumption estimation by genetic algorithm

This study presents a genetic algorithm (GA) with variable parameters to forecast electricity demand using stochastic procedures. The economic indicators used in this paper are price, value added, number of customers and consumption in the last periods. This model can be used to estimate energy demand in the future by optimizing parameter values using available data. The GA applied in this study has been tuned for all the GA parameters and the best coefficients with minimum error is finally found, while all the GA parameter values are tested together. The estimation errors of genetic algorithm model are less than that of estimated by regression method. Finally, analysis of variance (ANOVA) was applied to compare genetic algorithm, regression and actual data. It was found that at alpha = 0.05 the three treatments are not equal and therefore LSD method was used to identify which model is closer to actual data. Moreover, it showed that genetic algorithm has better estimated values for electricity consumption in Iranian agriculture sector.