吉林大学学报(工学版) ›› 2010, Vol. 40 ›› Issue (06): 1693-1697.

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Probabilistic prediction approach of heat load based on support vector interval regression

ZHANG Yong-ming,CHEN Lie,QI Wei-gui   

  1. Department of Electrical Engineering and Automation|Harbin Institute of Technology|Harbin 150001|China
  • Received:2008-12-01 Online:2010-11-01 Published:2010-11-01

Abstract:

A probabilistic prediction approach was proposed based on the support vector interval regression(SVIR). The initial parameters of the SVIR model are determined by the support vector regression, and its upper and lower bounds are identified by two radial basis function networks. The confidence intervals and the point prediction outputs can be obtained at the same time by the proposed approach. The validity and practicability of the proposed approach were proved by comparative simulations by the proposed model and the BP network model using the heat load data collected from a practical heat supply station.

Key words: heat supply energysaving, load prediction, support vector regression, support vector interval regression, confidence interval

CLC Number: 

  • TP29
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