Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm

作     者：Zhang, Weiping Niu, Peifeng Li, Guoqiang Li, Pengfei 

作者机构：Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Qinhuangdao Inst Technol Qinhuangdao 066100 Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Peoples R China 

出 版 物：《KNOWLEDGE-BASED SYSTEMS》 (知识库系统)

年 卷 期：2013年第39卷

页      面：34-44页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Steam turbine Least squares support vector machine Online learning Gravitational search algorithm Heat rate 

摘      要：Accurate heat rate forecasting is very important in ensuring the economic, efficient, and safe operation of a steam turbine unit. The support vector machine (SVM) is a novel tool from the artificial intelligence field that has been successfully applied to heat rate forecasting. The least squares SVM (LS-SVM) is an improved algorithm based on the SVM. LS-SVM has minimal computational complexity and fast calculation. However, traditional LS-SVM, which was established by using offline data samples, can no longer accurately describe the actual system working condition, thereby resulting in problems when directly used in heat rate prediction. In this paper, a heat rate forecasting method based on online LS-SVM, which possesses dynamic prediction functions, is proposed. To avoid blindness and inaccuracy in parameter selection, the gravitational search algorithm (GSA) is used to optimize the regularization parameter gamma and the kernel parameter sigma(2) of the online LS-SVM modeling. The results confirm the efficiency of the proposed method. (c) 2012 Elsevier B.V. All rights reserved.