排灌机械工程学报
   首页  学报介绍  编 委 会  作者园地  征订启事  编校法规  编读往来  录用公告  广告合作   行业新闻  留  言  English 
排灌机械工程学报  2011, Vol. 29 Issue (4): 352-358    DOI: 10.3969/j.issn.1674—8530.2011.04.016
节水灌溉工程 最新目录 | 下期目录 | 过刊浏览 | 高级检索 Previous Articles  |  Next Articles  
基于联合时序的混沌时用水量短期预测调度
张琴1, 汪雄海1, 朱庆建2
( 1. 浙江大学电气学院,浙江 杭州 310027; 2. 华信邮电咨询设计研究院,浙江 杭州 310014)
Short- term prediction of chaotic hourly water consumption based on united time series
Zhang Qin1,Wang Xionghai1,Zhu Qingjian2
(1.Institute of Electric Engineering,Zhejiang University,Hangzhou,Zhejiang 310027,China;2.Huaxin P&T Consulting and Designing Institute Co.Ltd.,Hangzhou,Zhejiang 310014,China)
 全文: PDF (600 KB)   HTML (1 KB)   输出: BibTeX | EndNote (RIS)      背景资料
摘要 针对给水优化调度的时用水量高精度短期预测难题,提出一种基于横向分时段和纵向残差修正的联合时序短期混沌预测方法.经模式识别获得关联度高的横向时序为研究样本,重构横向分时段相空间并分析典型时段流量数据的混沌特性,建立混沌预测模型,以在线最小二乘支持向量机作为混沌预测工具得出各时段用水流量;为了能动态跟踪用水突变,实时采集纵向数据序列做残差计算,进而用灰色模型进行残差预测修正以提高预测精度.依据杭州市萧山某大用户时用水量实例,对一段时间的正常用水和突变用水做连续24 h短期混沌预测,在不同方法下的时用水量预测精度作深入对比研究.仿真结果表明:该预测方法对这一类混沌时序短期预测具有良好的计算精度,能很好反映各典型用水日的特点和实时用水变化情况,预测效果明显优于其他预测方法,更易满足供水优化调度的需求.
服务
把本文推荐给朋友
加入我的书架
加入引用管理器
E-mail Alert
RSS
作者相关文章
张琴
汪雄海
朱庆建
关键词时用水量   短期预测   最小二乘支持向量机   横向分时段   混沌特性   纵向残差修正     
Abstract:  Aiming at the high short-term prediction accuracy of hourly water consumption in water supply optimal operation,a united time series method was proposed based on horizontal period clustering and vertical residual modification.The horizontal time series were determined as research samples by pattern recognition with high relevancy.After the phase space of horizontal period clustering was restructured,chaotic characteristics of typical period data were analyzed and chaotic prediction model was established.Least square support vector machine was used as a forecasting tool to obtain period flows.Furthermore,to track water consumptions dynamically,vertical residuals were modified by grey model prediction after collecting real-time data.The period historical data from Xiaoshan were supplied in the normal and abnormal case study to forecast the day-ahead hourly water consumption,and prediction accuracy with different methods were compared.Test results show that the method is adept in the short-term forecasting this kind of chaotic time series,which reflects the characters of typical days and real-time water variations.And the new method outperforms other methods obviously,thus it can better satisfy the demands for optimal operation of water distribution system.
Key words hourly water consumption   short-term prediction   least squares support vector machine   horizontal period clustering   chaotic characters   vertical residual modification   
收稿日期: 2011-03-21; 出版日期: 2011-07-30
基金资助:

国家973计划项目(2009CB320602);浙江省重点专项项目(2006C11227)

通讯作者: 汪雄海( 1948-) , 男, 浙江龙游人, 教授, 博士生导师( 通信作者, wxh_10@ zju.edu.cn) , 主要从事智能调度优化策略研究.   
作者简介: 张 琴( 1982-) , 女, 山西阳泉人, 博士研究生( deavor@ gmail.com) , 主要从事复杂系统优化控制策略与节能降耗技术研究.
引用本文:   
张琴, 汪雄海, 朱庆建. 基于联合时序的混沌时用水量短期预测调度[J]. 排灌机械工程学报, 2011, 29(4): 352-358.
ZHANG Qin, Wang-Xiong-Hai, Zhu-Qing-Jian. Short- term prediction of chaotic hourly water consumption based on united time series[J]. Journal of Drainage and Irrigation Machinery Engin, 2011, 29(4): 352-358.
 
[1] Alvisi S, Franchini M, Marinelli A. A short-term, pattern based model for water-demand forecasting [ J ]. Journal of Hydrology,2007,9 ( 1 ) :39 - 52.
[2] 于景华,田立新.混沌时间序列及其在能源系统中的应用[J].江苏大学学报:自然科学版,:.
[3] 赵鹏,张宏伟.城市用水量的混沌特性与预测[J].中国给水排水,2008,24(5):90.
[4] 秦奕青,蔡卫东,杨炳儒.非线性时间序列的相空间重构技术研究[J].系统仿真学报,2008,20(11):2969-2973.
[5] 邰能灵,侯志俭.小波模糊神经网络在电力系统短期负荷预测中的应用[J].中国电机工程学报,:.
[6] 柳景青,张土乔.渊度时用水量预测的分时段混沌建模方法[J].浙门人学学报工学版,2005,39(1):11-15.
[7] KIDSll O. Stream flow forecasting using different artifi- cial neural network algorithms [ J ]. Journal of Hydrologic Engineering ,2007,532 - 539.
[8] l,i Tianliang,He l.iming,l.i Haipeng. Prediction and a- nalysis of chaotic time series on the basis of support vee-tor[ J ]. Journal of Systems Engineering and Electronics, 2008,19(4) :806 - 811.
[9] Gato S,Jayasuriya N, Roberts P. Forecasting residential water demand: Case study [J]. Journal of Water Re-sources Planning and Management, 2007,133 ( 4 ) : 309 -319.
[10] Towler E,Rajagopalan B,Summers R S,et al. An approach for probabilistic ibrecasting of seasonal turbiditythreshold exceedance [ J ]. Water Resources Research,2010,46 ( 6 ) , W06511 , DOI :10.1029/2009 WR007834.
没有找到本文相关文献

江苏大学梦溪校区(镇江市梦溪园巷30号)图书馆5楼 0511-84440893 传真0511--84440033
Copyright 江苏大学杂志社 2010-2015 All Rights Reserved