State Grid Corporation of China, Shanghai Electric Power Company
College of Electric Power Engineering, Shanghai University of Electric Power

【目的】为挖掘用户侧节能减排潜力,对用户用电行为进行精细化分析和管理,提升电能利用效率,提出了一种基于事件监测和高斯混合模型聚类相结合的负荷特征提取方法。【方法】首先通过基于滑动窗的事件监测算法提取每个电器单次运行时的有功功率波动,借助事件监测算法可获得电器的启动时间、次数和运行时长。其次针对同一电器常出现功率相近但运行状态不一致问题,利用高斯混合模型聚类算法“软分类”、类簇灵活的优势,对负荷工作状态进行精细分类,形成符合用电设备实际运行情况的负荷状态特征库。最后采用公开数据集AMPds2作为研究对象,采用所提出的方法研究居民用户的用能习惯,并开展验证分析,与其他模型进行对比。【结果】结果表明所提的方法能够更好地提取负荷特征。

【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 status 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.

国网上海市电力公司项目资助项目(20222302837C188)

李宾皑,李凡,周德生,等.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.

LI Bin’ai,LI Fan,ZHOU Desheng,et al.Research on event monitoring and load feature extraction method based on residential electricity consumption data[J].Journal of Taiyuan University of Technology,2024,55(1):2-11.