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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.
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关键词:
- 供热节能 /
- 负荷预报 /
- 混沌 /
- Volterra自适应滤波器
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.-
Keywords:
- heat supply energy-saving /
- load prediction /
- chaos /
- Volterra adaptive filter
[1] Jiang Y 2006 HVAC 36 37(in Chinese)[江 亿 2006 暖通空调 36 37]
[2] [3] Rios-Moreno G J, Trejo-Perea M, Castaneda-Miranda R, Hernandez-Guzman V M, Herrera-Ruiz G 2007 Automation in Construction 16 713
[4] [5] Dodier R H, Henze G P 2004 J. Solar Energy Eng. 2 19
[6] [7] Li Z Q, Zhu D H, Liu D Y 2007 HVAC 37 1(in Chinese)[黎展求、朱栋华、刘冬岩 2007 暖通空调 37 1]
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[10] [11] [12] Zhang J S, Xiao X C 2001 Acta Phys. Sin. 50 1248(in Chinese)[张家树、肖先赐 2001 50 1248]
[13] [14] Li H C, Zhang J S, Xiao X C 2005 Chin. Phys. 14 2181
[15] Zhao H Q, Zhang J S 2009 Neural Networks 22 1471
[16] [17] [18] Henry D, Abarbanel N M, Rabinovich M I, Evren T 2001 Phys. Lett. A 281 368
[19] Kennel M B, Brown R, Abarbanel H D I 1992 Phys. Rev. A 45 3403
[20] [21] Grassberger P, Procaccia I 1983 Phys. D 9 189
[22] [23] [24] Grassberger P, Procaccia I 1983 Phys. Rev. Let. 50 346
[25] [26] Wolf A, Swift J B, Swinney H L 1985 Phys. D 16 285
[27] Rosenstein M T, Collins J J, De Luca C J 1993 Phys. D 65 117
[28] -
[1] Jiang Y 2006 HVAC 36 37(in Chinese)[江 亿 2006 暖通空调 36 37]
[2] [3] Rios-Moreno G J, Trejo-Perea M, Castaneda-Miranda R, Hernandez-Guzman V M, Herrera-Ruiz G 2007 Automation in Construction 16 713
[4] [5] Dodier R H, Henze G P 2004 J. Solar Energy Eng. 2 19
[6] [7] Li Z Q, Zhu D H, Liu D Y 2007 HVAC 37 1(in Chinese)[黎展求、朱栋华、刘冬岩 2007 暖通空调 37 1]
[8] [9] Zhang J S, Xiao X C 2000 Acta Phys. Sin. 49 403(in Chinese)[张家树、肖先赐 2000 49 403]
[10] [11] [12] Zhang J S, Xiao X C 2001 Acta Phys. Sin. 50 1248(in Chinese)[张家树、肖先赐 2001 50 1248]
[13] [14] Li H C, Zhang J S, Xiao X C 2005 Chin. Phys. 14 2181
[15] Zhao H Q, Zhang J S 2009 Neural Networks 22 1471
[16] [17] [18] Henry D, Abarbanel N M, Rabinovich M I, Evren T 2001 Phys. Lett. A 281 368
[19] Kennel M B, Brown R, Abarbanel H D I 1992 Phys. Rev. A 45 3403
[20] [21] Grassberger P, Procaccia I 1983 Phys. D 9 189
[22] [23] [24] Grassberger P, Procaccia I 1983 Phys. Rev. Let. 50 346
[25] [26] Wolf A, Swift J B, Swinney H L 1985 Phys. D 16 285
[27] Rosenstein M T, Collins J J, De Luca C J 1993 Phys. D 65 117
[28]
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