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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.
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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]
[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] -
[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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