Neural network intelligent learning algorithm for inter-related energy products applications

Abstract

Accurate prediction of energy products future price is required for effective reduction of future price uncertainty as well as risk management. Neural Networks (NNs) are alternative to statistical and mathematical methods of predicting energy product prices. The daily prices of Propane (PPN), Kerosene Type Jet fuel (KTJF), Heating oil (HTO), New York Gasoline (NYGSL), and US Coast Gasoline (USCGSL) interrelated energy products are predicted. The energy products prices are found to be significantly correlated at 0.01 level (2- tailed). In this study, NNs learning algorithms are used to build a model for the accurate prediction of the five (5) energy product price. The aptitudes of the five (5) NNs learning algorithms in the prediction of PPN, KTJF, HTO, NYGSL, and USCGSL are examined and their performances are compared. The five (5) NNs learning algorithms are Gradient Decent with Adaptive learning rate backpropagation (GDANN), Bayesian Regularization (BRNN), Scale Conjugate Gradient backpropagation (SCGNN), Batch training with weight and bias learning rules (BNN), and Levenberg-Marquardt (LMNN). Results suggest that the LMNN and BRNN can be viewed as the best NNs learning algorithms in terms of R2 and MSE whereas GDANN was found to be the fastest. Results of the research can be use as a guide to reduce the high level of uncertainty about energy products prices, thereby provide a platform for developmental planning that can result in the improvement economic standard.