Research on Event Monitoring and Load Feature Extraction Method Based on Residential Electricity Consumption Data

Abstract

Purposes A load feature extraction method based on a combination of event monitoring and Gaussian mixture model clustering is proposed to explore the potential of energy saving 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. Methods 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 algorithm. Second, to address the problem that the same appliance often has similar power but inconsistent 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.

Keywords

• event monitoring
• gaussian mixture model clustering
• residential load
• load classification
• unsupervised clustering