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[1]赖祥威,夏云霓,郑万波,等.基于集成学习的改进灰色瓦斯浓度序列预测*[J].中国安全生产科学技术,2021,17(7):16-21.[doi:10.11731/j.issn.1673-193x.2021.07.003]
　LAI Xiangwei,XIA Yunni,ZHENG Wanbo,et al.Improved grey prediction of gas concentration sequence based on integrated learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(7):16-21.[doi:10.11731/j.issn.1673-193x.2021.07.003]
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基于集成学习的改进灰色瓦斯浓度序列预测*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
17
期数:
2021年7期
页码:
16-21
栏目:
学术论著
出版日期:
2021-07-31

文章信息/Info

Title:
Improved grey prediction of gas concentration sequence based on integrated learning
文章编号:
1673-193X(2021)-07-0016-06
作者:
赖祥威; 夏云霓; 郑万波; 崔俊飞; 吴燕清; 史耀轩
(1.昆明理工大学 理学院,云南 昆明 650500；
2.昆明理工大学 数据科学研究中心,云南 昆明 650500；
3.重庆大学 计算机学院，重庆 400030;
4.中煤科工集团重庆研究院有限公司，重庆 400037；
5.瓦斯灾害监控与应急技术国家重点实验室，重庆 400037；
6.重庆大学 资源及安全学院，重庆 400030）
Author(s):
LAI Xiangwei;  XIA Yunni;  ZHENG Wanbo;  CUI Junfei;  WU Yanqing;  SHI Yaoxuan
(1.Faculty of Science,Kunming University of Science and Technology;
2.Data Science Research Center,Kunming University of Science and Technology;
3.School of Computer Science,Chongqing University;
4.Chongqing Research Institute Co.,Ltd.,China Coal Technology & Industry Group;
5.State Key Laboratory of Gas Disaster Monitoring and Emergency Technology;
6.College of Resources and Safety,Chongqing University)
关键词:
瓦斯浓度; 时间序列; 传统灰色预测; 改进灰色预测; 集成学习
Keywords:
gas concentration;  time sequence;  traditional grey prediction;  improved grey prediction;  integrated learning
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2021.07.003
文献标志码:
A
摘要:
为有效提高煤矿瓦斯浓度动态预测精度，基于微分方程理论和最小二乘法，从灰色预测模型静态灰色作用量出发，优化灰色作用量，推导幂指数型灰色作用量的改进灰色瓦斯浓度预测算法，推导基于集成学习不同灰色作用量幂指数型灰色瓦斯预测模型，进而研究吉林八连城长期和短期瓦斯浓度监控数据预测精度。结果表明：瓦斯浓度时间序列近似线性时，基于集成学习的改进灰色瓦斯浓度预测算法优于传统灰色瓦斯浓度预测算法，使瓦斯浓度预测值和实际值的均方根误差降低，均方根差最大降低2.25%。研究结果可有效提瓦斯浓度预测精度。
Abstract:
In order to effectively improve the dynamic prediction accuracy of coal mine gas concentration,based on the theory of differential equations and the least square method,starting from the static gray action of the gray prediction model,the gray action is optimized,and an improved grey gas concentration prediction algorithm of power exponential gray action is derived.Based on the ensemble learning of the power exponential gray gas prediction model of different gray effects,the prediction accuracy of the long-term and short-term gas concentration monitoring data in Balian city of Jilin is studied.The results show that when the gas concentration time series is approximately linear,the improved gray gas concentration prediction algorithm based on integrated learning,is better than the traditional gray gas concentration prediction algorithm,which reduces the root mean square error between the predicted value and the actual value of the gas concentration,and maximizes the root mean square difference decrease by 2.25%.The research results can effectively improve the accuracy of gas concentration prediction.

参考文献/References:

［1］杨丽,刘晖,毛善君，等.基于多元分布滞后模型的瓦斯浓度动态预测［J］.中国矿业大学学报,2016,45(3):455-461.YANG Li,LIU Hui,MAO Shanjun,et al.Dynamic gas concentration prediction based on multivariate distributed lag model ［J］.Journal of China University of Mining and Technology,2016,45(3):455-461.
［2］胡坤,王素珍,韩盛,等.基于TLBO-LOIRE的回采工作面瓦斯涌出量预测［J］.应用基础与工程科学学报,2017,25(5):1048-1056.HU Kun,WANG Suzhen,HAN Sheng,et al.Prediction of gas emission from stope face based on tlbo-loire ［J］.Journal of Applied Foundation and Engineering Science,2017,25(5):1048-1056.
［3］李冬,彭苏萍,杜文凤,等.煤层瓦斯突出危险区综合预测方法［J］.煤炭学报,2018,43(2):466-472.LI Dong,PENG Suping,DU Wenfeng,et al.Comprehensive prediction method for coal seam gas outburst hazard zone ［J］.Acta Coal Sinica,2018,43(2):466-472.
［4］李成武,付帅,解北京,等.煤与瓦斯突出能量预测模型及其在平煤矿区的应用［J］.中国矿业大学学报,2018,47(2):231-239.LI Chengwu,FU Shuai,XIE Beijing,et al.Coal and gas outburst energy prediction model and its application in pingdingshan coal mining area ［J］.Journal of China University of Mining and Technology,2018,47(2):231-239.
［5］张友谊,崔金雷,焦向东.煤与瓦斯突出多指标耦合预测模型研究及应用［J］.工程科学学报,2018,40(11):1309-1316.ZHANG Youyi,CUI Jinlei,JIAO Xiangdong.Research and application of multi-index coupling prediction model for coal and gas outburst ［J］.Journal of Engineering Science,2008,40(11):1309-1316.
［6］JIA P,LIU H,WANG S,et al.Research on a mine gas concentration forecasting model based on a gru network［J］.IEEE Access,2020,8(2):38023-38031.
［7］XUE G,SONG J,KONG X,et al.Prediction of natural gas consumption for city-level dhs based on attention gru:a case study for a Northern Chinese city［J］.IEEE Access,2019,7(9):130685-130699.
［8］唐泽斯,郭进,王金贵,等.基于人工神经网络的气体泄爆最大超压预测研究［J］.中国安全生产科学技术,2020,16(4):56-62.TANG Zesi,GUO Jin,WANG Jingui,et al.Research on prediction of maximum overpressure of gas leakage based on artificial neural network ［J］.Journal of Safety Science and Technology,2020,16(4):56-62.
［9］LIU Y,XUED,PAN F,et al.Hybrid gray model based on fractional order gray model and verhulst model［C］//2019 Chinese Control and Decision Conference,Nanchang,China,2019.
［10］ZHAO J,AN K.Research on multi-variable grey prediction model for icing thickness［C］//2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2),Changsha,China,2019.
［11］DONG J,ZHANG X,DENG M,et al.Research on power prediction of photovoltaic electric field based on grey model［C］//2019 Chinese Control Conference,Guangzhou,China,2019.
［12］ZHANGY,SUN H,GUO Y.Wind power prediction based on pso-svr and grey combination model［J］.IEEE Access,2019,7(9):136254-136267.
［13］WU L,LIU X,QI Y,et al.Propagation path loss prediction based-on grey verhulst model［C］//2018 IEEE/ACIS 17th International Conference on Computer and Information Science,Singapore,2018.
［14］熊远南.基于改进灰色-多元回归组合预测模型的燃煤电厂智慧水务研究［J］.化工进展,2020,39(S2):393-400.XIONG Yuannan.Research on intelligent Water Supply for coal-fired power Plants based on improved Gray-multiple regression combination Prediction Model ［J］.Progress in Chemical Engineering,2020,39(S2):393-400.
［15］王建良,李孥.中国东中西部地区天然气需求影响因素分析及未来走势预测［J］.天然气工业,2020,40(2):149-158.WANG Jianliang,LI Nu.Analysis of influencing factors of natural gas demand in eastern and central regions of china and forecast of future trend ［J］.Natural Gas Industry,2020,40(2):149-158.

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备注/Memo

备注/Memo:
收稿日期： 2020-12-19
* 基金项目： 国家科技重大专项（2016ZX05045-004）；重庆市社会事业与民生保障科技创新专项（CSTC2016SHMSZX90002）
作者简介： 赖祥威，硕士研究生，主要研究方向为机器学习以及瓦斯预警大数据。
通信作者： 郑万波，博士，副研究员，主要研究方向为大数据与云计算、矿山应急管理、工程物探、测控技术与仪器。
更新日期/Last Update: 2021-08-05