Presenting the Prediction Model of Iran’s Electricity Annual Consumption by means of Narx Neural Network and Studying Effect of Targeted Subsidies on it

Document Type : Original Article

Authors

1 Professor of Management, Shahid Beheshti University, Department Of Management And Accounting

2 MA in Information Technology Engineering

3 Ph.D. Candidate, Shahid Beheshti University, Department of Management And Accounting

Abstract

In this research a model has been designed for predicting Iran’s electricity annual consumption based on economic criteria and by means of artificial neural networks. Moreover, the effect of implementing targeted subsidies plan on annual electricity consumption of Iran has been studied. In such a way that Narx neural network takes Iran population and GDP variables as input and generates Iran annual electricity consumption as output. For testing and teaching the designed network, data related to the years 1362 to 1389 were collected and the last 4 years data has been collected for testing the network performance. For studying the accuracy degree of designed network, Perceptron neural network model and Arima time series model also were designed .And comparison of results shows that Narx neural network is more powerful in predicting Iran electricity consumption. Considering the key factors affecting electricity consumption in this model, Iran electricity consumption in the years before implementing the targeted subsidies could be predicted with a high accuracy and therefore the amount of electricity consumption in Iran during the year 1390, 91 and 92 shows the effect of subsidies targeting plan on Iran electricity consumption in first year of implementing it. The tangible decrease in electricity consumption in that year in comparison to designed model is a bare witness for this effect. Findings of research shows that according to Narx neural network and the graduate effect of time on it ,this model could be used for predicting the annual consumption of electricity inside the country.

Keywords

References

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