A Fractional Discrete Grey Model with Particle Swarm Optimizer and Its Applications in Forecasting the Gasoline Consumption in Chongqing China
Journal of Advances in Mathematics and Computer Science · pp. 54–61 · Published 14 May 2021
10.9734/jamcs/2021/v36i430356Abstract
Forecasting gasoline consumption is of great significance for formulating oil production, foreign trade policies, and ensuring the balance of domestic refined oil supply. Based on grey system theory, a fractional accumulation operator is constructed to optimize the accumulation method of the traditional discrete grey model, and the Particle Swarm Optimization algorithm is used to solve the fractional nonlinear parameters. This model was used in the prediction of gasoline consumption in Chongqing, China, and compared with the existing 7 models. The results show that the fractional discrete grey model optimized by PSO has better prediction accuracy. The fractional discrete grey model optimized by PSO can be used as a quantitative method in the field of energy forecasting.
Cited by 0
No indexed citations yet.
Related research
- Combining a Continuous Search Algorithm with a Discrete Search Algorithm for Solving Non-linear Bi-level Programming Problem — shares topic coverage
- A Novel Method for Optimizing Fractional Grey Prediction Model — shares topic coverage
- Prophecy of Stock Price of Bharat Immunological & Biological Corporation Ltd using Hybrid ANN and PSO Model — shares topic coverage
- Data Clustering Using Hybridization Strategies of Continuous Ant Colony Optimization, Particle Swarm Optimization and Genetic Algorithm — shares topic coverage
- An Improved Hybrid Algorithm for Optimizing the Parameters of Hidden Markov Models — shares topic coverage
Article metrics
Real usage data collected on this platform.
15
Page views
0
PDF downloads
0
Outbound clicks
0
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.