公开(公告)号：US20240085465A1

公开(公告)日：2024-03-14

申请号：US17974494

申请日：2022-10-26

Applicant: INSTITUTE FOR INFORMATION INDUSTRY

Inventor： KUANG-PING TSENG ,  WEN-JEN HO ,  YUNG-CHIEH HUNG ,  KUEI-CHUN CHIANG

IPC: G01R22/06

CPC classification number: G01R22/06

Abstract: A method and a system for identifying an operating status of an electrical appliance based on non-intrusive load monitoring are provided. The method includes the following steps. Total power consumption history data of a target field and appliance power consumption history data of target electrical appliances are obtained. The appliance power consumption history data of each target electrical appliance is converted into a binary data set. The total power consumption history data is clustered into cluster samples to obtain first feature data sets, which are then dimensionally reduced into second feature data sets, and a machine learning model is trained by using the second feature data sets and the binary data sets of the target electrical appliances to establish an operation identification model for the target electrical appliances. The operation identification model identifies an operating status of the target electrical appliances according to total power consumption data.

公开(公告)号：US20240085466A1

公开(公告)日：2024-03-14

申请号：US17973488

申请日：2022-10-25

Applicant: INSTITUTE FOR INFORMATION INDUSTRY

Inventor： SU-AN LIU ,  KUEI-CHUN CHIANG ,  YUNG-CHIEH HUNG

IPC: G01R22/10 ,  G06F1/28

CPC classification number: G01R22/10 ,  G06F1/28

Abstract: A power consumption behavior analyzing device and a power consumption behavior analyzing method are provided. The power consumption behavior analyzing method includes: generating, according to power consumption data, power consumption curves of household ends, and extracting feature points; acquiring household data records corresponding to the households, respectively; performing a correlation analysis according to the household data records and the power consumption data of the feature points to find household features corresponding to correlations of the feature points as key features based on a correlation threshold value; clustering, according to the key features, total power consumption data to obtain a plurality of household power consumption characteristic curves and a plurality of power consumption patterns; and calculating similarities respectively between a power consumption curve of a to-be-analyzed household end and the household power consumption characteristic curves, and marking the to-be-analyzed household end as a corresponding power consumption pattern.