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

CHEN Qi-song; CHEN Xiao-wei; ZHANG Xin; QI Lin; WU Mao-nian

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

更新时间：2026-09-04

- Main Stream Temperature Forecasting Model Based on QPSO and SVM in Power Plant
- Vol. 27, Issue 7, Pages: 218-221(2010)
- 作者机构：
  
  1. 贵州大学计算机科学与技术学院,贵州,贵阳,550025
  2. 贵州省光电子技术与应用重点实验室,贵州,贵阳,550025
  3. 贵州电力培训中心,贵州,贵阳,550025
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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陈其松, 陈孝威, 张欣, 戚琳, 吴茂念. 基于粒子群优化支持向量机的火电厂主汽温预测模型[J]. 微电子学与计算机, 2010, 27(7): 218-221,224.

CHEN Qi-song, CHEN Xiao-wei, ZHANG Xin, et al. Main Stream Temperature Forecasting Model Based on QPSO and SVM in Power Plant[J]. 2010, 27(7): 218-221.
陈其松, 陈孝威, 张欣, 戚琳, 吴茂念. 基于粒子群优化支持向量机的火电厂主汽温预测模型[J]. 微电子学与计算机, 2010, 27(7): 218-221,224. DOI：

CHEN Qi-song, CHEN Xiao-wei, ZHANG Xin, et al. Main Stream Temperature Forecasting Model Based on QPSO and SVM in Power Plant[J]. 2010, 27(7): 218-221. DOI：

摘要

针对支持向量机在大规模训练中算法收敛速度慢、复杂程度高等问题

采用量子粒子群算法选取最小二乘支持向量机的模型参数

避免了人为选择参数的盲目性

提高了预测模型的训练速度和泛化能力.实验结果表明

该算法具有容易实现、节省计算成本、提高收敛速度等优点

应用于火电锅炉主汽温预测模型

取得良好的效果.

Abstract

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.

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