Evaluating Hybrid Model of Neural Networks and Genetic Algorithms in the Forecast Energy Consumption in the Transportation Sector

Energy, besides other factors, production is considered the main factor in growth and economic development, and economics can play beneficial roles in the performance of different sectors. Hence, the country authorities should try to predict anything more precisely regarding energy consumption in the proper planning and guidance of consumption to control the way they desire energy demand and supply parameters. This paper aims to evaluate the hybrid model of artificial neural networks and genetic algorithms in forecasting demand energy to predict energy consumption in the country. A case study is energy consumption in Iran's transportation sector. So, for this review, we use the annual data on 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 vehicles as the input variables. Evaluation results showed that the model of Artificial hybrid model of Neural Networks (ANN) and Genetic Algorithm (GA), campared to other models, has the highest accuracy in predicting energy demand in the transportation sector.