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首页> 外文期刊>Energy >A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China
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A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China

机译：灰狼优化器的分数时滞灰色模型及其在重庆天然气和煤炭消费量预测中的应用

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摘要

Introduction of the fractional order accumulation has made significant contributions to the development of forecasting methods, and fractional grey models play a key role in such new methods. However, the fractional grey models may also be inaccurate in some cases as they do not consider the time delayed effect. To further improve the applicability of the existing fractional grey models, a novel fractional grey model called the fractional time delayed grey model is proposed in this paper. The essence of the fractional time delayed term is discussed, revealing that the fractional time delayed term is essentially a function between the polynomial functions with integer order, which can be more flexible to improve the modelling accuracy. The cutting-edge Grey Wolf Optimizer is introduced to find the optimal value of fractional order. Detailed modelling procedures, including the computational steps and the intelligent optimization algorithm, have been clearly presented. Four real world case studies are used to validate the effectiveness of the proposed model, in comparison with 8 existing grey models. Finally the proposed model is applied to forecast the coal and natural gas consumption of Chongqing China, the results show that the proposed model significantly outperforms the other 8 existing grey models. (C) 2019 Elsevier Ltd. All rights reserved.
机译：分数阶累积的引入为预测方法的发展做出了重大贡献，分数灰度模型在这种新方法中起着关键作用。但是，分数灰色模型在某些情况下也可能不准确，因为它们没有考虑时间延迟的影响。为了进一步提高现有分数灰度模型的适用性，提出了一种新颖的分数灰度模型，称为分数时间延迟灰度模型。讨论了分数时间延迟项的本质，揭示了分数时间延迟项本质上是整数函数多项式函数之间的函数，可以更灵活地提高建模精度。引入了最先进的Gray Wolf Optimizer，以找到分数阶的最佳值。详细的建模过程，包括计算步骤和智能优化算法，已经清晰呈现。与8个现有的灰色模型相比，使用了四个实际案例研究来验证所提出模型的有效性。最后，将该模型用于预测重庆市的煤炭和天然气消费量，结果表明该模型明显优于其他8种现有的灰色模型。 （C）2019 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Energy》 |2019年第1期|487-507|共21页
  • 作者

    Ma Xin; Mei Xie; Wu Wenqing; Wu Xinxing; Zeng Bo;

  • 作者单位

    Southwest Univ Sci & Technol, Sch Sci, Mianyang, Sichuan, Peoples R China|Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu, Sichuan, Peoples R China;

    Southwest Univ Sci & Technol, Sch Sci, Mianyang, Sichuan, Peoples R China|Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China;

    Southwest Univ Sci & Technol, Sch Sci, Mianyang, Sichuan, Peoples R China;

    Southwest Petr Univ, Sch Sci, Chengdu, Sichuan, Peoples R China|Southwest Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu, Sichuan, Peoples R China;

    Chongqing Technol & Business Univ, Coll Business Planning, Chongqing, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Energy consumption forecasting; Energy economics; Fractional grey model; Grey wolf optimizer; Five-year-plan;

    机译：能耗预测;能源经济学;分数阶灰色模型;灰太狼优化器;五年计划;

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