Study on Prediction Model for Boiler Thermal Efficiency Based on Support Vector Regression and Particle Swarm Optimization

Abstract

Aiming at the problem that the boiler thermal efficiency can't be on-line calculated in the field, data collected are divided into training data and testing data by researching on the 1000MW unit of Sanbaimen Power Plant in Datang Chaozhou, and then prediction model for boiler thermal efficiency is established by applying support vector regression and particle swarm optimization. This method uses optimization function of particle swarm algorithm to optimize the parameters C and g of the prediction model, and applys test data and the randon data to test the model's accuracy and generalization capability. The simulation results show that prediction model for boiler thermal efficiency has higher pre- diction accuracy that relative error is controlled within 1% and has better generalization ability, which provides feasible scheme for online measurement of boiler thermal efficiency.