[1]王 芳,马素霞,王 河.基于随机森林变量选择的飞灰含碳量 预测模型[J].热力发电,2018,(11):89-95.[doi:10.19666/j.rlfd.201803085]
 WANG Fang,MA Suxia,WANG He.Prediction model of carbon content in fly ash using random forest variable selection method[J].Thermal Power Generation,2018,(11):89-95.[doi:10.19666/j.rlfd.201803085]
点击复制

基于随机森林变量选择的飞灰含碳量 预测模型

参考文献/References:

[1] 王春林, 周昊, 周樟华, 等. 基于支持向量机的大型电厂锅炉飞灰含碳量建模[J]. 中国电机工程学报, 2005, 25(20): 72-76.
WANG Chunlin, ZHOU Hao, ZHOU Zhanghua, et al. Support vector machine modeling on the unburned carbon in fly ash[J]. Proceedings of the CSEE, 2005, 25(20): 72-76.
[2] 陈敏生, 刘定平. 基于核主元分析和支持向量机的电站锅炉飞灰含碳量软测量建模[J]. 华北电力大学学报, 2006, 33(1): 72-75.
CHEN Minsheng, LIU Dingping. Soft-sensing modeling of the unburned carbon in fly ash based on KPCA-SVM for power station boilers[J]. Journal of North China Electric Power University, 2006, 33(1): 72-75.
[3] 周国雄, 李琳, 沈学杰. 基于SVM和灰色预测的飞灰含碳量集成预测[J]. 系统仿真学报, 2013, 25(4): 727-731.
ZHOU Guoxiong, LI Lin, SHEN Xuejie. Integrated prediction for carbon content in fly ash based on online support vector machine and grey prediction[J]. Journal of System Simulation, 2013, 25(4): 727-731.
[4] 麻红波, 余瑞锋, 倪艳红, 等. 基于GSA-LSSVM的循环流化床锅炉飞灰含碳量预测[J]. 锅炉技术, 2016, 47(2): 53-56.
MA Hongbo, YU Ruifeng, NI Yanhong, et al. The prediction of the carbon content of fly ash of circulating fluidized bed boilers based on GSA-LSSVM[J]. Boiler Technology, 2016, 47(2): 53-56.
[5] 赵新木, 王承亮, 吕俊复, 等. 基于BP 神经网络的煤粉锅炉飞灰含碳量研究[J]. 热能动力工程, 2005, 20(2): 158-162.
ZHAO Xinmu, WANG Chengliang, LV Junfu, et al. Study on carbon content in fly ash of pulverized coal boiler based on BP neural network[J]. Journal of Engineering for Thermal Energy and Power, 2005, 20(2): 158-162.
[6] 陈强, 王培红, 李琳, 等. 电站锅炉飞灰含碳量在线软测量模型算法[J]. 电力系统自动化, 2005, 29(2): 45-49.
CHEN Qiang, WANG Peihong, LI Lin, et al. An online soft sensing model algorithm for carbon content of fly ash in utility boilers[J]. Automation of Electric Power Systems, 2005, 29(2): 45-49.
[7] 陈敏生, 刘定平. 电站锅炉飞灰含碳量的优化控制[J].动力工程, 2005, 25(4): 545-549.
CHEN Minsheng, LIU Dingping. Optimized control of carbon content in utility boilers’ fly ash[J]. Journal of Power Engineering, 2005, 25(4): 545-549.
[8] 牛培峰, 化克, 张现平. 基于信息融合技术的锅炉飞灰含碳量测控系统[J]. 仪器仪表学报, 2009, 30(6): 1207-1210.
NIU Peifeng, HUA Ke, ZHANG Xianping. Measurement and control system for unburned carbon in fly ash based on information fusion techniques[J]. Journal of Scientific Instrument, 2009, 30(6): 1207-1210.
[9] 卞和营, 王军敏. 支持向量回归在飞灰含碳量软测量中的应用[J]. 计算机测量与控制, 2014, 22(2): 345-348.
BIAN Heying, WANG Junmin. Application research on support vector regression in soft sensor of carbon content in fly ash[J]. Computer Measurement & Control, 2014, 22(2): 345-348.
[10] 卞和营, 方彦军. 基于支持向量回归的飞灰含碳量软测量[J]. 热力发电, 2014, 43(10): 46-51.
BIAN Heying, FANG Yanjun. SVR-based study on soft-sensing measurement of carbon content in fly ash[J]. Thermal Power Generation, 2014, 43(10): 46-51.
[11] 冯旭刚, 钱家俊, 章家岩. 基于遗传神经网络敏感度分析的飞灰含碳量测量方法[J]. 电子测量与仪器学报, 2016, 30(7): 1083-1089.
FENG Xugang, QIAN Jiajun, ZHANG Jiayan. Prediction method of unburned carbon content in fly ash based on genetic neural network with sensitivity analysis[J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(7): 1083-1089.
[12] 叶小岭, 顾荣, 邓华, 等. 基于WRF模式和PSO-LSSVM的风电场短期风速订正[J]. 电力系统保护与控制, 2017, 45(22): 48-54.
YE Xiaoling, GU Rong, DENG Hua, et al. Modification technology research of short-term wind speed in wind farm based on WRF model and PSO-LSSVM method[J]. Power System Protection and Control, 2017, 45(22): 48-54.
[13] 孟安波, 胡函武, 刘向东. 基于纵横交叉算法优化神经网络的负荷预测模型[J]. 电力系统保护与控制, 2016, 44(7): 102-106.
MENG Anbo, HU Hanwu, LIU Xiangdong. Short-term load forecasting using neural network based on wavelets and crisscross optimization algorithm[J]. Power System Protection and Control, 2016, 44(7): 102-106.
[14] 王宁, 谢敏, 邓佳梁, 等. 基于支持向量机回归组合模型的中长期降温负荷预测[J]. 电力系统保护与控制, 2016, 44(3): 92-97.
WANG Ning, XIE Min, DENG Jialiang, et al. Mid-long term temperature-lowering load forecasting based on combination of support vector machine and multiple regression[J]. Power System Protection and Control, 2016, 44(3): 92-97.
[15] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[16] 赵腾, 王林童, 张焰, 等. 采用互信息与随机森林算法的用户用电关联因素辨识及用电量预测方法[J]. 中国电机工程学报, 2016, 36(3): 604-614.
ZHAO Teng, WANG Lintong, ZHANG Yan, et al. Relation factor identification of electricity consumption behavior of users and electricity demand forecasting based on Mutual Information and Random Forests[J]. Proceedings of the CSEE, 2016, 36(3): 604-614.
[17] 吴潇雨, 和敬涵, 张沛, 等. 基于灰色投影改进随机森林算法的电力系统短期负荷预测[J]. 电力系统自动化, 2015, 39(12): 50-55.
WU Xiaoyu, HE Jinghan, ZHANG Pei, et al. Power system short-term load forecasting based on improved random forest with grey relation projection[J]. Automation of Electric Power Systems, 2015, 39(12): 50-55.
[18] 侯俊雄, 李琦, 朱亚杰, 等. 基于随机森林的PM2.5实时预报系统[J]. 测绘科学, 2017, 42(1): 1-6.
HOU Junxiong, LI Qi, ZHU Yajie, et al. Real-time forecasting system of PM2.5 concentration based on spark framework and random forest model[J]. Science of Surveying and Mapping, 2017, 42(1): 1-6.
[19] YANG X S. Flower pollination algorithm for global optimization[J]. Unconventional Computation and Natural Computation Proceedings, 2012, 7445: 240-249.
[20] SAYED A E F, NABIL E, BADR A. A binary clonal flower pollination algorithm for feature selection[J]. Pattern Recognition Letters, 2016, 77: 21-27.
[21] 徐勇刚, 张建业, 龚小刚, 等. 基于改进随机森林算法的电力业务实时流量分类方法[J]. 电力系统保护与控制, 2016, 44(24): 82-89.
XU Yonggang, ZHANG Jianye, GONG Xiaogang, et al. A method of real-time traffic classification in secure access of the power enterprise based on improved random forest algorithm[J]. Power System Protection and Control, 2016, 44(24): 82-89.
[22] 王月兰, 马增益, 尤海辉, 等. 基于自适应神经模糊推理系统的煤粉锅炉飞灰含碳量建模[J]. 热力发电, 2018, 47(1): 26-32.
WANG Yuelan, MA Zengyi, YOU Haihui, et al. Modeling for unburned carbon content in fly ash from coal-fired boilers basedon adaptive neuron-fuzzy inference system [J]. Thermal Power Generation, 2018, 47(1): 26-32.
[23] 方必武, 刘涤尘, 王波, 等. 基于小波变换和改进萤火虫算法优化LSSVM 的短期风速预测[J]. 电力系统保护与控制, 2016, 44(8): 37-43.
FANG Biwu, LIU Dichen, WANG Bo, et al. Short-term wind speed forecasting based on WD-CFA-LSSVM model[J]. Power System Protection and Control, 2016, 44(8): 37-43.
(责任编辑 杜亚勤)

相似文献/References:

[1]王 键,傅钟泉,蒲良毅,等.燃煤锅炉脱硫装置新型烟气换热器设计及性能分析[J].热力发电,2009,(08):10.
 WANG Jian,FU Zhong-quan,PU Liang-yi,et al.DESIGN AND DERFORMANCE ANALYSIS OF GAS HEAT EXCHANGER CONCERNING DESULPHURIZATION SYSTEM OF COAL-FIRED BOILER[J].Thermal Power Generation,2009,(11):10.
[2]周新刚.锅炉热效率试验中的煤质元素分析计算模型[J].热力发电,2009,(05):0.
[3]赵勇纲,王永红,王 琪.煤掺烧二次燃料存在的问题及对策[J].热力发电,2006,(05):0.
[4]盛昌栋,张 军.煤粉锅炉共燃生物质发电技术的特点和优势[J].热力发电,2006,(03):0.
[5]王春昌.燃煤锅炉新三区低Nox燃烧技术的研究探讨[J].热力发电,2005,(04):0.
[6]何 滔,李佛金.沙角C电厂2100t/h锅炉结渣原因分析与运行调整措施[J].热力发电,2003,(01):0.
[7]巨林仓,范伊波,胡 勇,等.一种自适应神经网络控制器的研究[J].热力发电,2000,(04):0.
[8]张方炜,刘海玉,熊小鹤,等.模糊层次分析法在燃煤锅炉NOx排放影响因素定量分析中的应用[J].热力发电,2010,(01):14.
 ZHANG Fang-wei,LIU Hai-yu,XIONG Xiao-he,et al.APPLICATION OF FHAP IN QUANTITATIVE ANALYSIS OF AFFECTING FACTORS UPON NOx EMISSION FROM COAL-FIRED BOILERS[J].Thermal Power Generation,2010,(11):14.
[9]李名武.扬州第二发电厂MPS磨煤机掺磨石子煤的实践[J].热力发电,2010,(02):62.
 LI Ming-wu.PRACTICE OF MIXEDLY PULVERIZING PYRITES IN MPS COAL PULVERIZER[J].Thermal Power Generation,2010,(11):62.
[10]胡长兴,周劲松,何 胜,等.我国典型电站燃煤锅炉汞排放量估算[J].热力发电,2010,(03):1.
 HU Chang-xing,ZHOU Jin-song,HE Sheng,et al.ESTIMATION OF MERCURY EMISSION FROM COAL-FIRED BOILERS IN TYPICAL POWER PLANTS OF CHINA[J].Thermal Power Generation,2010,(11):1.

备注/Memo

王芳(1989—),女,博士研究生,主要研究方向为燃煤机组节能优化,wangfang0024@link.tyut.edu.cn。

更新日期/Last Update: 2018-10-25