[1]张照鑫,朱允刚,虞玉峰,等.基于贝叶斯网和集成学习的智能电表状态评价[J].计算机技术与发展,2021,31(06):146-151.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 026]
　ZHANG Zhao-xin,ZHU Yun-gang,YU Yu-feng,et al.State Evaluation of Smart Energy Meter Based on BayesianNetwork and Integrated Learning[J].,2021,31(06):146-151.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 026]
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基于贝叶斯网和集成学习的智能电表状态评价()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年06期
页码:
146-151
栏目:
应用前沿与综合
出版日期:
2021-06-10

文章信息/Info

Title:
State Evaluation of Smart Energy Meter Based on BayesianNetwork and Integrated Learning
文章编号:
1673-629X(2021)06-0146-06
作者:
张照鑫; 朱允刚; 虞玉峰; 赵山博; 张胜男; 陶紫涵
吉林大学 计算机学院,吉林 长春 130023
Author(s):
ZHANG Zhao-xin; ZHU Yun-gang; YU Yu-feng; ZHAO Shan-bo; ZHANG Sheng-nan; TAO Zi-han
School of Computer,Jilin University,Changchun 130023,China
关键词:
智能电表; 贝叶斯网络; 集成学习; 状态评价; 聚类算法; 凸函数证据理论
Keywords:
smart energy meter;  Bayesian network;  integrated learning;  state evaluation;  clustering algorithm;  convex functionevidence theory
分类号:
TP181
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 06. 026
摘要:
智能电能表随着社会的发展和其采集信息的便利性而日益普及,同时每年对智能电表进行的状态判断与检测过程也耗费了大量的人力物力资源。 因此,为了高效准确地进行智能电表状态的判断,课题组提出利用智能电表运行数据构建贝叶斯网络来实现精准推断的方法。 首先根据生活经验和相关文献确定能反映智能电能表状态的主要因素,然后选取相关因素的代表性数据构成电能表状态评价数据集,并对该数据集进行离散化,然后利用贝叶斯网作为融合模型解决智能电能表相关数据中存在的不确定性和不完备性问题。 同时结合选择性集成学习方法进一步提高状态评价的准确性,利用聚类算法选取每组中的最优贝叶斯网络结构,然后基于凸函数证据理论对各贝叶斯网的决策结果进行进一步融合,最终实现对问题表计的高准确判定。
Abstract:
With the development of society and the convenience of collecting information,smart electric energy meters are increasingly popular. At the same time, the state judgment and detection process of smart electric meters consume a lot of human and material resources every year. Therefore,in order to efficiently and accurately judge the state of the smart meter,the research group has proposed a method to construct a Bayesian network using smart meter operation data to achieve accurate inference. First the main factors that can reflect the state of the smart energy meter based on life experience and related literature are determined,then representative data of related factors are selected to form the energy meter state evaluation data set which is discretized,and then Bayesian network is used as the fusion model to solve the problems of uncertainty and incompleteness in the data related to smart energy meters. At the same time,combined with selective integrated learning methods to further improve the accuracy of state evaluation,the clustering algorithm is used to select the optimal Bayesian network structure in each group,and then based on the convex function evidence theory,the decision results of each Bayesian network are further merged. Finally a high accuracy judgment of the problem meter can be achieved.

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更新日期/Last Update: 2021-06-10