Chaotic property analysis and prediction model study for heating load time series
供热负荷时间序列混沌特性分析及预报模型研究

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.