一种新的二次多项式离散灰色模型在中国能源消费预测中的应用

IF 1.4 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY
Xin Ma, Wenqing Wu, Yuanyuan Zhang
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引用次数: 0

摘要

离散灰色建模技术是一种新的灰色预测模型方法,可以有效地提高灰色模型的有效性和适用性。为了建立更通用、更有效的单变量灰色预测模型,本文利用离散灰色建模技术建立二次多项式离散灰色模型,简称QPDGM。讨论了QPDGM模型的性质,表明该模型可以看作是传统的离散灰色模型和非齐次灰色模型的扩展,并且与三类指数序列吻合。最后运用QPDGM模型对中国能源消费进行了预测,包括电力、原油和天然气消费。结果与常用的单变量灰色预测模型进行了比较,表明QPDGM模型总体上比其他模型更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of a novel quadratic polynomial discrete grey model to forecast energy consumption of China
The discrete grey modelling technique is a novel methodology of grey predictionmodels, which is effective to improve the effectiveness and applicability of greymodels. In order to build a more general and effective univariate grey predictionmodel, the discrete grey modelling technique is utilised in this paper to builda quadratic polynomial discrete grey model, abbreviated as the QPDGM. Theproperties of the QPDGM model have been discussed, which indicate that thenew model can be regarded as an extension of the conventional discrete greymodel and nonhomogeneous grey model, and it is also coincidence with threeclasses of exponential sequences. The QPDGM model is finally applied to predictthe energy consumption of China, including the electric power, crude oil andnatural gas consumptions. The results have been compared to some commonlyused univariate grey prediction models, which indicates the QPDGM model isgenerally more accurate than other models.
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来源期刊
Scientia Iranica
Scientia Iranica 工程技术-工程:综合
CiteScore
2.90
自引率
7.10%
发文量
59
审稿时长
2 months
期刊介绍: The objectives of Scientia Iranica are two-fold. The first is to provide a forum for the presentation of original works by scientists and engineers from around the world. The second is to open an effective channel to enhance the level of communication between scientists and engineers and the exchange of state-of-the-art research and ideas. The scope of the journal is broad and multidisciplinary in technical sciences and engineering. It encompasses theoretical and experimental research. Specific areas include but not limited to chemistry, chemical engineering, civil engineering, control and computer engineering, electrical engineering, material, manufacturing and industrial management, mathematics, mechanical engineering, nuclear engineering, petroleum engineering, physics, nanotechnology.
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