Research on a grey prediction model based on energy prices and its applications

Abstract

Energy is the material basis on which human beings depend for survival and development and a strong guarantee for the sustainable development of national economies. Accurately predicting energy prices can better regulate the allocation of energy markets, improve the level of scientific energy control for energy users, and promote the sound and healthy development of the energy economy. Based on the existing market economic model of energy prices, this paper first studies the relationship between the energy supply and demand and its price and establishes a dynamic differential equation. According to the difference information from the differential equation and difference equation, a dynamic grey prediction model of the energy price is established. Then, the parameter estimation and approximate reduction formula of the model are studied. To improve the accuracy of the model, the particle swarm optimization algorithm is used to optimize the background value of the novel model, establish the optimized grey forecast model of energy prices, and obtain the modelling process of the optimized model. Finally, the model is applied to the prediction of fuel prices in three typical regions: Singapore, the Mediterranean Sea and the Arabian Gulf. Three kinds of grey prediction models are selected for comparative analysis, and five criteria are used for evaluation. The results show that the average relative errors of the simulations and predictions of the three actual cases are all less than 2%; the five evaluation criteria also basically indicate that the novel model is better than the other three grey prediction models.