Main Stream Temperature Forecasting Model Based on QPSO and SVM in Power Plant

According to the low speed of constringency and high complexity of training methods in SVM large scales training, quantum-behaved particle swarm algorithm (QPSO) is presented to solve the problem. Parameters selection is an important problem in the research area of support vector machines. Meanwhile, quantum-behaved particle swarm algorithm is used to choose the parameters of least square support vector machines, which can avoid the man-made blindness and enhance the efficiency and capability of forecasting. The experimental results indicate that this QPSO-SVM forecasting model can be trained quickly and good generalization, is easy to be realized, can save the calculating cost and improve the constringency speed.