PREDICATION OF THE CALORIFIC VALUE FOR FUEL COAL BASED ON THE SUPPORT VECTOR REGRESSION MACHINE WITH PARAMETERS OPTIMIZED BY GENETIC ALGORITHM

JIANG Wenhao; WEI Hongqi; QU Tianzhang; ZHU Feng College of Energy and Environment Engineering

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PREDICATION OF THE CALORIFIC VALUE FOR FUEL COAL BASED ON THE SUPPORT VECTOR REGRESSION MACHINE WITH PARAMETERS OPTIMIZED BY GENETIC ALGORITHM

更新时间：2025-09-28

- PREDICATION OF THE CALORIFIC VALUE FOR FUEL COAL BASED ON THE SUPPORT VECTOR REGRESSION MACHINE WITH PARAMETERS OPTIMIZED BY GENETIC ALGORITHM
- Thermal Power Generation Vol. 40, Issue 3, Pages: 14-19(2011)
- 作者机构：
  
  东南大学能源与环境学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TK16
- Published：2011
- 稿件说明：
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JIANG Wenhao, WEI Hongqi, QU Tianzhang, et al. PREDICATION OF THE CALORIFIC VALUE FOR FUEL COAL BASED ON THE SUPPORT VECTOR REGRESSION MACHINE WITH PARAMETERS OPTIMIZED BY GENETIC ALGORITHM[J]. Thermal power generation, 2011, 40(3): 14-19.
DOI：

JIANG Wenhao, WEI Hongqi, QU Tianzhang, et al. PREDICATION OF THE CALORIFIC VALUE FOR FUEL COAL BASED ON THE SUPPORT VECTOR REGRESSION MACHINE WITH PARAMETERS OPTIMIZED BY GENETIC ALGORITHM[J]. Thermal power generation, 2011, 40(3): 14-19. DOI：

摘要

利用支持向量回归机(SVR)和遗传算法(GA)对煤的低位发热量建模

采用遗传算法对支持向量机预测模型的各项参数进行寻优

为减少所选参数对训练样本的依赖性

引入交叉验证的思想

以推广能力最好的一组参数作为最终参数。将优化参数代入SVR模型

得到基于遗传算法的支持向量回归机(GA-SVR)模型。通过对电厂入炉煤的试验数据进行分析

并且与常规SVR模型和BP神经网络模型(BP-ANN)进行对比

以验证该模型的可靠性和精确性。结果表明

该方法可准确地预测燃煤发热量。

Abstract

By using the Support Vector Regression(SVR) machine and the genetic algorithm(GA)

a model for predicting the lower calorific value of fuel coal has been established

and the parameters of SVR machine model being optimized through GA.In order to reduce the dependency of selected parameters upon the training samples

the cross-validation ideas have been introduced

the parameters with best generalization performance being chosen as the final ones

these optimal parameters are input into the SVR machine model

and the genetic algorithm-support vector regression(GS-SVR) model has been obtained.Through analysis of the test data for furnace-entering coal in one power plant

and the comparison with the convintional SVR model and BP-ANN model

the reliability and accuracy of GA-SVR model have been verified

the results show that the said model can accurately predict the calorific value of fuel coals.

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