Authors: Wang Meiping; Tian Qi
Addresses: College of Environment Science and Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, China ' College of Environment Science and Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, China
Abstract: Considering the questions of complex non-linearity, large thermal inertia, retardance of a district heating system, it is very difficult to establish accurate mathematical models of heating parameters prediction for the heating system. Correlation analysis of influence factors is used to obtain the major factors influencing heating parameters through analysing operational data of a heating system; these factors serve as input parameters of the predicting model. This paper describes a prediction method that combines Support Vector Machine (SVM) with neural network. The method creates a network structure between heating parameters and its influence factors. Evaluation indexes of relative error and correlation coefficients are given to analyse the feasibility of the method within the scope of engineering applications through using the network model to regress and predict the heating parameters and compare them with testing data. It turned out that the prediction technique provides powerful guidance for operation of the district heating system.
Keywords: parameter prediction; heating parameters; support vector machines; SVM; nonlinear; neural networks; district heating; mathematical modelling.
International Journal of Wireless and Mobile Computing, 2015 Vol.8 No.3, pp.294 - 300
Received: 18 Jul 2014
Accepted: 16 Sep 2014
Published online: 13 May 2015 *