Title :
Prediction of the NOx emissions from thermal power plant based on support vector machine optimized by genetic algorithm
Author :
Zhou, Jianguo ; Liang, Huaitao
Author_Institution :
Sch. of Bus. & Adm., North China Electr. Power Univ., Baoding, China
Abstract :
With the development of thermal power industry, statistics on the NOx emissions become important. In this paper, based on the traditional support vector machine model, we establish support vector machine model optimized by genetic algorithm, improve the prediction accuracy of SVM model. Use the NOx emissions data from 1995 to 2009, predict the NOx emissions from thermal power plant in the year of 2010, and verify the reasonableness of the GA-SVM model.
Keywords :
genetic algorithms; nitrogen compounds; pollution; power engineering computing; support vector machines; thermal power stations; GA-SVM model; NOx; NOx emission prediction; genetic algorithm; prediction accuracy; support vector machine; thermal power plant; Gallium; Genetic algorithms; Mathematical model; Optimization; Power generation; Predictive models; Support vector machines; NOx emissions; genetic algorithm; support vector machine; thermal power plant;
Conference_Titel :
Information and Financial Engineering (ICIFE), 2010 2nd IEEE International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-6927-7
DOI :
10.1109/ICIFE.2010.5609441
