Forecast consumption energy of Iran using Hybrid model of artificial neural networks and genetic algorithms and Compared with traditional methodes

Mirfakhraddiny, Seyed Heydar; BabaeiMeybodi, Hamid; Morovati Sharifabadi, Ali

Forecast consumption energy of Iran using Hybrid model of artificial neural networks and genetic algorithms and Compared with traditional methodes

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Abstract

During recent decades, Energy as one of the most important factors of production and also as one of the most important end products, a special place in the country's economic development and growth development. Hence, the country authorities should try to predict anything more precise energy consumption in the proper planning and guidance consumption, to control the way they desired energy demand and supply parameters. The purpose of this paper is Evaluation Hybrid model of artificial neural networks and genetic algorithms in the forecast consumption energy of Iran. Therefore in this study, data from the annual energy consumption as the output forecasting model range and was used as input variables, data of the annual total population, GDP, imports and exports.The end results were assessed with of different models (RSE), (ME) and (RMSE). Evaluation results showed that the hybrid model of neural networks and genetic algorithm (ANN-GA), compared to other models with the highest accuracy in predicting consumption energy of Iran.

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