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Modelling and forecasting of Jiangsu's total electricity consumption using the novel grey multivariable model

机译:利用新型灰色多变量模型建模与预测江苏总电消耗

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Electricity demand prediction plays an important role in the policy makings and plans for the governments, energy sector investors and other relevant stakeholders. Although there exist several forecasting techniques, selection of the most appropriate technique is of great importance. One of the forecasting techniques which has proved successful in prediction is GM(1, N). In order to clarify the interaction mechanism of driving variables and improve the accuracy of the model, a new model which is based on the development trend of multiple driving variables, abbreviated as TMGM (1, N), is proposed. Firstly, a new forecast model of the development trend of the driving variables is established in order to make better use of the interaction mechanism of the driving variables. On the basis of that, the new grey model TMGM (1, N) is constructed. Meanwhile, the solution to the model parameters are derived on the least square method. And the time response formula is solved by the convolution integral to make up the defects of the solving method of traditional model GM(1, N). Finally, a real application about the forecast of the total electricity consumption in Jiangsu Province is used to demonstrate the feasibility and practicability of the TMGM(1, N) model. The results indicate the superiority of TMGM(1, N) model when compared with GM(1, N) model and TGM(1, N) model.
机译:电力需求预测在政府,能源部门投资者和其他相关利益攸关方的政策制备和计划中起着重要作用。虽然存在多种预测技术,但选择最合适的技术是非常重要的。在预测中成功的预测技术之一是GM(1,N)。为了澄清驱动变量的相互作用机制,提高了模型,该模型基于多个驱动变量,缩写为TMGM(1,N)的发展趋势一个新的模型的准确性,提出了。首先,建立了驱动变量的发展趋势的新预测模型,以便更好地利用驱动变量的相互作用机制。在此基础上,构建了新的灰色模型TMGM(1,N)。同时,在最小二乘法中导出模型参数的解决方案。并且时间响应公式通过卷积积分来解决,以构成传统模型GM的求解方法的缺陷(1,N)。最后,关于江苏省总电力消费预测的实际应用程序用于证明TMGM(1,N)模型的可行性和实用性。结果表明,与GM(1,N)模型和TGM(1,N)模型相比,TMGM(1,N)模型的优越性。

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