[1]张明光,刘连国,王磊,等.基于神经网络的燃气小时负荷预测[J].山东建筑大学学报,2010,(02):206-209.
 ZHANG Ming-guang,LIU Lian-guo,WANG lei,et al.Gas hour’s load forecasting based on neural network[J].Journal of Shandong jianzhu university,2010,(02):206-209.
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基于神经网络的燃气小时负荷预测()
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《山东建筑大学学报》[ISSN:1673-7644/CN:37-1449/TU]

卷:
期数:
2010年02期
页码:
206-209
栏目:
工程实践
出版日期:
2010-04-15

文章信息/Info

Title:
Gas hour’s load forecasting based on neural network
作者:
张明光1刘连国2王磊1王洋1田贯三1
1.山东建筑大学 热能工程学院, 山东 济南 250101;2.荣成市建设工程勘察设计审查中心,山东 荣成 264300
Author(s):

ZHANG Ming-guang1 LIU Lian-guo2WANG lei1et al

1.School of Thermal Energy Engineering, Shandong Jianzhu University, Jinan 250101, China;2.Rongcheng Inspection Center for Constrction Project and Design, Rongcheng 264300,China
关键词:
燃气管网小时负荷BP模型负荷预测
Keywords:
gas pipeline hour’s load BP model load forecasting
分类号:
TU996.3
文献标志码:
A
摘要:
燃气小时负荷预测对于保证管网的用气量以及管网的运行调度和优化配置具有重要作用。通过对小时负荷特性及其影响因素的分析,采用三层BP前馈神经网络,利用VC 6.0++编制的运算程序对燃气小时负荷进行预测。预测结果表明:该模型具有较好的精度,可以满足负荷预测的需要,具有较好的应用前景。
Abstract:
Gas hour’s load forecasting plays an important role in gas consumption, the operation of scheduling and optimization in the pipeline network. Through analysis of the hour’s load characteristics and influencing factors. Three-layer BP feed forward neural network, and VC 6.0 + + procedures are employed for the preparation of the computing load on the gas-hour forecasts. Prediction results show that the model bears high accuracy, which meets the needs of load forecasting and has a good application prospect.

相似文献/References:

[1]尹贻林 郭慧岩 付聪.基于GIS的燃气管网泄漏扩散后果分析及模拟[J].山东建筑大学学报,2009,(06):514.
 YIN Yi-lin GUO Hui-yan FU Cong.Consequence analysis and simulation of leakage diffusion of gas pipeline based on GIS[J].Journal of Shandong jianzhu university,2009,(02):514.

更新日期/Last Update: 2010-05-20