Point and Interval Forecasting of Coal Price Adopting a Novel Decomposition Integration Model

• Published in: Energies (Basel) 2024-08, Vol.17 (16), p.4166
• Main Authors: Liu, Junjie, Liu, Lang
• Format: Article
• Language: English
• Subjects: Accuracy; Air quality management; Algorithms; Analysis; Coal; Crude oil prices; Deep learning; Econometrics; Energy prices; Forecasting; importance analysis; Machine learning; N-BEATS; Neural networks; point and interval forecasting; Prices and rates; Scale models; Supply and demand; Support vector machines; Sustainable development; Time series; variational modal decomposition
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17164166

Accurate and trustworthy forecasting of coal prices can offer theoretical support for the rational planning of coal industry output, which is of great importance in ensuring a stable and sustainable energy supply and in achieving carbon neutrality targets. This paper proposes a novel decomposition integration model, called VCNQM , to perform point and interval forecasting of coal price by a combination of variational modal decomposition (VMD), chameleon swarm algorithm (CSA), N-BEATS, and quantile regression. Initially, the variational modal decomposition is enhanced by the chameleon swarm algorithm for decomposing the coal price sequence. Then, N-BEATS is used to forecast each subsequence of coal prices, integrating all results to obtain a point forecast of coal prices. Next, interval forecasting of coal prices is achieved through quantile regression. Finally, to demonstrate the superiority of the VCNQM model’s prediction, we make a cross-comparison about predictive performance between the VCNQM model and other benchmark models. According to the experimental findings, we demonstrate the following: after the decomposition by CSA-VMD, the coal price subseries’ fluctuation is significantly weakened; using quantile regression provides a reliable interval prediction, which is superior to point prediction; the predicted interval coverage probability (PICP) is higher than the confidence level of 90%; the share power industry index and coal industry index have the greatest impact on coal prices in China; compared to these benchmark models, the VCNQM model’s prediction errors are all reduced. Therefore, we conclude that when forecasting coal prices, the VCNQM model has an accurate and reliable prediction.