Evaluation Hybrid model of Neural Networks and Genetic Algorithms in the Forecast Energy Consumption the Transportation Sector
DOI:
https://doi.org/10.22105/tw1j6z02Keywords:
Energy consumption, Multivariate regression, Artificial neural networks, Genetic algorithmAbstract
Energy besides Other factors production is considered the main factor in the growth and economic development and in the performance of different sectors economic can play beneficial roles. 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 demand energy. for Prediction energy consumption in the country. Case study is energy consumption in transportation sector of Iran. So for this review, were used the annual data energy consumption of transport as a variable output of forecast models and data from the entire country's annual population, GDP and the number of vehicle as the input variables. 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 energy demand in the transportation sector.
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