供热负荷时间序列混沌特性分析及预报模型研究  

Chaotic property analysis and prediction model study for heating load time series

作　　者：张永明[1] 齐维贵[1] 

机构地区：[1]哈尔滨工业大学电气工程及自动化学院,哈尔滨150001

出　　处：《物理学报》2011年第10期98-107,共10页Acta Physica Sinica

基　　金：国家"十一五"科技支撑计划重大项目(批准号:2006BAJ03A04)资助的课题~~

摘　　要：为揭示供热负荷时间序列蕴含的内在动态特性,采用非线性分析方法对供热负荷时间序列混沌特性进行识别.以集中供热热源和热力站负荷时间序列为研究对象,进行相空间重构,求得了饱和关联维数和最大Lyapunov指数,验证了供热负荷时间序列的混沌特性,为供热负荷预报研究提供了混沌理论基础.针对现有供热负荷预报方法多为主观模型方法,本文提出了一种基于Volterra自适应滤波器的供热负荷预报方法,该方法不必事先建立主观模型,而直接根据负荷序列本身的特性进行预报,避免了负荷预报的人为主观性.最后,给出了供热负荷预报算例,仿真结果表明二阶Volterra自适应滤波器模型预报精度较高,可满足供热工程节能控制及热力调度的需要.In order to reveal the internal dynamics characteristics of heating load time series,the existing chaotic behavior is validated by use of nonlinear analysis method.The data sets taken from heat source and substation of district heat supply are studied by which phase spaces are reconstructed,and the correlation dimensions and the largest Lyapunov exponent are computed to identify the presence of chaos in heat load time series.By the analysis of the results,chaotic characteristics obviously exist in the heat load time series,which is a theoretical basis for the correlative investigation of heat load prediction.According to the existing heat load predictive method almostly based subjective models,a novel predictive approach based on Volterra adaptive filter,which avoids the subjective model assumptions,is presented for heat load prediction.Finally the predictive results are presented,and the simulation results illustrate that the second-order Volterra adaptive filter has high predictive accuracy which can meet the demands of heat energy-saving control and heat dispatching in practical applications.

关 键 词：供热节能 负荷预报 混沌 Volterra自适应滤波器 

分 类 号：TU995[建筑科学—供热、供燃气、通风及空调工程]