文章基本信息

标题：A Forecast Model of City Natural Gas Daily Load Based on Data Mining
本地全文：下载
作者：Liang Chen ; Jijun Zhang
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2022
卷号：2022
DOI：10.1155/2022/1562544
语种：English
出版社：Hindawi Publishing Corporation
摘要：Data mining technology is more and more widely used in the daily load forecasting of natural gas systems. It is still difficult to carry out high-precision, timely intraday load forecasting and intraday load dynamic characteristics clustering for natural gas systems. Based on data mining technology, this paper proposes a stable intraday load forecasting method for the natural gas flow state-space model. The load sensitivity under the current operating conditions of the system is obtained by calculation; the sample space of the state space is established through data processing; the partitions under different clustering radii are calculated; and the best intraday load flow is obtained through the state space effectiveness evaluation method. The experimental results show that the model load forecasting accuracy and relative error reached 98.5% and 0.026, respectively, which solved the problem of processing the long-term accumulated historical data of gas intra-day load. At the same time, the amount of data calculation was reduced by 33.6%, which effectively promoted the quantification of intraday load influencing factors and qualitative analysis.