太原理工大学学报2024，Vol.55Issue(1)：2-11,10.DOI:10.16355/j.tyut.1007-9432.20230541
居民用电数据的事件监测与负荷特征提取方法研究
Research on Event Monitoring and Load Feature Extraction Method Based on Residential Electricity Consumption Data
摘要
Abstract
[Purposes]A load feature extraction method based on a combination of event moni-toring and Gaussian mixture model clustering is proposed to explore the potential of energy sav-ing and emission reduction at the customer side,to finely analyze and manage the customers'electricity consumption behavior,and to improve the efficiency of electricity utilization.[Meth-ods]First,the active power fluctuation of each appliance during a single operation is extracted by the event monitoring algorithm based on sliding window,and the start-up time,number of times,and operation duration of the appliance can be obtained by the event monitoring algo-rithm.Second,to address the problem that the same appliance often has similar power but incon-sistent operation status,the Gaussian mixture model clustering algorithm with the advantages of"soft classification"and flexible class clusters is adopted to finely classify the load operating sta-tus and form a load status feature library that is consistent with the actual operation of power-using equipment.Finally,with the public data set AMPds2 as the research object,the method proposed in this paper is applied to study the energy consumption habits of residential customers,and the validation analysis is carried out.[Findings]The results show that the proposed method can extract load features better than other models.关键词
事件监测/高斯混合模型聚类/居民负荷/负荷分类/无监督聚类Key words
event monitoring/Gaussian mixture model clustering/residential load/load clas-sification/unsupervised clustering分类
信息技术与安全科学引用本文复制引用
李宾皑,李凡,周德生,曾平,杨秀,闫钟宇..居民用电数据的事件监测与负荷特征提取方法研究[J].太原理工大学学报,2024,55(1):2-11,10.基金项目
国网上海市电力公司项目资助项目(20222302837C188) （20222302837C188）
