Institute for Information Industry

Kuei-Chun Chiang

Yung-Chieh Hung

Wen-Jen Ho

Chia-Wei Tsai

Kuang-Ping Tseng

Yung-Cheih Hung

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.

Method and system for identifying operating status of electrical appliance based on non-intrusive load monitoring

A power consumption analyzing server and a power consumption analyzing method thereof are provided. According to the user data, the power consumption analyzing server clusters users into a plurality of groups. For each group, the power consumption analyzing server generates M+1 number of encoded outputs by inputting total power consumption time sequence data corresponding to a reference user in the group into an autoencoder. For each group, the power consumption analyzing server receives an actual appliance measurement data from an energy management system apparatus of the reference user, and labels M number of encoded outputs of the M+1 number of encoded outputs to map them to M categories of appliances. Finally, for each group, the power consumption analyzing server establishes a non-intrusive load monitoring system model of the group.

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Taipei, Taiwan

Kuei-Chun Chiang