Title :
Forecasting NOx emissions in power plant using rough set and QGA-based SVM
Author :
Zhou, Jian-guo ; An, Yuan-yuan
Author_Institution :
Sch. of Bus. Adm., North China Electr. Power Univ., Baoding, China
Abstract :
NOx emissions prediction research is to the benefit of NOx emissions control. Studying the NOx emissions under new situation in coal-fired plant is of great significance. This paper introduces Quantum Genetic Algorithm (QGA) to optimize the parameters of SVM. Our experiment results demonstrate that using the QGA-SVM model will achieve better prediction than the individual SVM model.
Keywords :
coal; genetic algorithms; nitrogen compounds; power plants; power system control; rough set theory; support vector machines; NO; SVM; coal-fired plant; emissions control; power plant; quantum genetic algorithm; rough set; support vector machines; Positron emission tomography; Support vector machines; NOx Emissions; Quantum Genetic Algorithm (QGA); Rough Set; Support Vector Machines (SVM);
Conference_Titel :
Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6539-2
DOI :
10.1109/ICACTE.2010.5579299
