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Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. (English) Zbl 1222.78031

Summary: This study presents an integrated genetic algorithm (GA) and artificial neural network (ANN) to estimate and predict electricity demand using stochastic procedures. The economic indicators used in this paper are price, value added, number of customers and consumption in the previous periods. This model can be used to estimate energy demand in the future by optimizing parameter values. The GA applied in this study is tuned for all its parameters and the best coefficients with minimum error are identified, while all parameter values are tested concurrently. The estimation errors of genetic algorithm model are less than that of estimated by regression method. ANN is used to forecast each independent variable and then electricity consumption is forecasted up to year 2008. It is shown that the integrated GA and ANN dominate time series approach from the point of yielding less MAPE (Mean Absolute Percentage Error) error. In addition, another unique feature of this study is the utilization of ANN instead of time series to obtain better predictions for energy consumption. Electricity consumption in the Iranian agriculture sector from 1981 to 2005 is considered as the case of this study.

MSC:

78A55 Technical applications of optics and electromagnetic theory
78M25 Numerical methods in optics (MSC2010)
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