A hybrid optimized artificial intelligent model to forecast crude oil using genetic algorithm

Crude oil is one of the most critical energy commodities while with coal and natural gas are projected to provide roughly the 86% share of the total US primary energy supply in 2030. In this study, a novel hybrid optimum model based on artificial intelligent (AI) is proposed for world crude oil spot price forecasting. A three-layer feed forward neural network (FNN) model was used to model the oil price forecasting. Genetic algorithm (GA) is employed not only to improve the learning algorithm, but also to reduce the complexity in parameter space. GA optimizes simultaneously, the connection weights between layers and the thresholds. In addition, GA reduces the dimension of the feature space and eliminates irrelevant factors. For verification and testing, two main crude oil price series, West Texas intermediate (WTI) crude oil spot price, Brent crude oil spot price and Iran crude oil are used to test the effectiveness of the proposed optimized neural network. Results show that optimized model has advantages in comparison with conventional ANN in terms of accuracy, variability, model creation and model examination. Both simulated and actual data sets are used for comparison.