{"title":"利用分数阶累积时滞灰色模型预测一次能源消耗","authors":"L. Zeng","doi":"10.1080/13873954.2020.1859547","DOIUrl":null,"url":null,"abstract":"ABSTRACT Energy consumption prediction is a hot issue, which is of great significance to regional energy security. In the existing prediction research with small samples, the time delay characteristic of an energy consumption system in itself is often ignored. To reflect the time delay characteristic of an energy consumption system and accurately grasp its development trend, a novel nonlinear time delay grey model with fractional order accumulation is presented. The new model is utilized to forecast and analyze Guangdong’s primary energy consumption, in which the time delay parameter is ascertained by the grey correlation analysis method, and the other parameters are determined via particle swarm optimization. The results show the simulation accuracy of the new model is higher than those of the other 3 grey models, and the predicted results in the next three years can provide decision-making and theoretical reference for the relevant departments of Guangdong province.","PeriodicalId":49871,"journal":{"name":"Mathematical and Computer Modelling of Dynamical Systems","volume":"27 1","pages":"31 - 49"},"PeriodicalIF":1.8000,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13873954.2020.1859547","citationCount":"8","resultStr":"{\"title\":\"Forecasting the primary energy consumption using a time delay grey model with fractional order accumulation\",\"authors\":\"L. Zeng\",\"doi\":\"10.1080/13873954.2020.1859547\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Energy consumption prediction is a hot issue, which is of great significance to regional energy security. In the existing prediction research with small samples, the time delay characteristic of an energy consumption system in itself is often ignored. To reflect the time delay characteristic of an energy consumption system and accurately grasp its development trend, a novel nonlinear time delay grey model with fractional order accumulation is presented. The new model is utilized to forecast and analyze Guangdong’s primary energy consumption, in which the time delay parameter is ascertained by the grey correlation analysis method, and the other parameters are determined via particle swarm optimization. The results show the simulation accuracy of the new model is higher than those of the other 3 grey models, and the predicted results in the next three years can provide decision-making and theoretical reference for the relevant departments of Guangdong province.\",\"PeriodicalId\":49871,\"journal\":{\"name\":\"Mathematical and Computer Modelling of Dynamical Systems\",\"volume\":\"27 1\",\"pages\":\"31 - 49\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2021-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/13873954.2020.1859547\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical and Computer Modelling of Dynamical Systems\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1080/13873954.2020.1859547\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical and Computer Modelling of Dynamical Systems","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/13873954.2020.1859547","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Forecasting the primary energy consumption using a time delay grey model with fractional order accumulation
ABSTRACT Energy consumption prediction is a hot issue, which is of great significance to regional energy security. In the existing prediction research with small samples, the time delay characteristic of an energy consumption system in itself is often ignored. To reflect the time delay characteristic of an energy consumption system and accurately grasp its development trend, a novel nonlinear time delay grey model with fractional order accumulation is presented. The new model is utilized to forecast and analyze Guangdong’s primary energy consumption, in which the time delay parameter is ascertained by the grey correlation analysis method, and the other parameters are determined via particle swarm optimization. The results show the simulation accuracy of the new model is higher than those of the other 3 grey models, and the predicted results in the next three years can provide decision-making and theoretical reference for the relevant departments of Guangdong province.
期刊介绍:
Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems.
The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application.
MCMDS welcomes original articles on a range of topics including:
-methods of modelling and simulation-
automation of modelling-
qualitative and modular modelling-
data-based and learning-based modelling-
uncertainties and the effects of modelling errors on system performance-
application of modelling to complex real-world systems.