电力消费组合模型

V. Kalinchyk, V. Pobigaylo, V. Kalinchyk, Aleksandr Meita, O. Borychenko
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摘要

本文研究了电力负荷预测的模型和方法。结果表明,在已知的功耗管理方法中,基于预测估计的方法是首选的。对工业企业用电管理系统过程预测问题进行了分析。结果表明,采用自适应模型作为工业企业供电系统负荷运行预测的基础是可行的。对基于指数平滑法的电力消费预测自适应模型的分析表明,该模型效率高,对电力消费过程的变化具有较好的适应性。结果表明,预测的最大困难是过程发展中的突变情况。过程中的突然变化可能导致对预估系统参数的预先存在的定性关系的破坏。如果跳跃是预测系统从一个稳态到另一个稳态的过渡,则修正常数平滑的指数平滑模型对这种变化具有最好的适应性。同时,模型计算出的“脉冲”型变化具有一定的延迟,导致预报的标准误差增大。因此,模型对变化的反应变慢了。为了消除这种情况,提出了一种基于组合模型的预测方法。本文考虑了两种组合预测模型——预测结果联合处理组合模型和选择型组合模型。对所考虑的模型进行了实验研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combined models of electricity consumption
The article investigates models and methods of electric load forecasting. It is shown that among the known methods of power consumption management, preference is given to those based on the use of forecast estimates. The analysis of works devoted to the issues of forecasting the processes of power consumption management systems of industrial enterprises is carried out. It is shown that it is expedient to use adaptive models as a basis for operative forecasting of loads of power supply systems of industrial enterprises. Analysis of adaptive models of electricity consumption forecasting based on the method of exponential smoothing showed their high efficiency and good adaptability to changes in the process of electricity consumption. It is shown that the greatest difficulty in forecasting are cases of abrupt changes in the development of the process. Abrupt changes in the process can lead to a violation of pre-existing qualitative relationships of the parameters of the projected system. If the jump is the transition of the predicted system from one steady state to another, the model of exponential smoothing with correction of the constant smoothing has the best adaptability to this kind of change. At the same time, changes of the "pulse" type are worked out by the model with a certain delay, which leads to an increase in the standard error of the forecast. Therefore, the model's response to change slows down. To eliminate this circumstance, a forecasting procedure based on combined models is proposed. The paper considers two models of combined forecasting - a combined model of joint processing of forecasting results and a combined model of selective type. Experimental studies of the considered models are carried out.
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