HEAT LOAD PREDICTION FOR HEAT SUPPLY SYSTEM BASED ON RBF NEURAL NETWORK AND TIME SERIES CROSSOVER

摘要：

In order to improve the energy-saving efficiency, a novel heat load prediction method based on radial basis function neural network (KBF NN) and time series crossover is proposed according to the characteristics of heat supply process. The dimension of the input vector in the RBK NN model is established with autocorrelation method. Then the horizontal and vertical prediction models are constructed using the RBF neural network, respectively. And those two prediction models are combined to produce the crossover prediction model whose crossover weight coefficients are calculated through the least-squares method. The comparison of simulation results shows that the accuracy of crossover prediction is superior to horizontal and vertical predictions. In addition, the speed of crossover prediction based on RBF neural network is proved faster than the one with back propagation neural network (BP NN).

关键词： Heat supply Load prediction RBF neural network Time series crossover

作者: LIE CHEN QIAO-LING ZHANG WEI-GUI QI JUAN LI

作者单位: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, 150001, P.R.China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 784-788

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）