Institute for Information Industry

Yung-Chieh Hung

Chia-Wei Tsai

Kuei-Chun Chiang

Wen-Jen Ho

Chun-Wei Yang

Shu-Wei Lin

Fang-Yi Chang

Kuang-Ping Tseng

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 model building device and a loading disaggregation system are provided. The model building device disaggregates total aggregated data outputted from a total electricity meter and measured during a unit processing period. The model building device includes a usage pattern-analyzing module, an information mapping module, and a time series-analyzing module. After receiving the total aggregated data, the usage pattern-analyzing module analyzes the total aggregated data based on detection conditions and generates usage pattern information accordingly. The information mapping module maps the usage pattern information to form encoded data. The time series-analyzing module analyzes time correlation of the encoded data to generate synthesized simulation data.

Model building device and loading disaggregation system

The present invention provides a method for acquiring the load operation probability of an electric power consumer and a method for acquiring the load operation probability of electric power consumer groups. The load information and the total power consumption information of an electric power consumer and the load information and the total power consumption information of electric power consumer groups can be given. The time-segment power consumption information of an electric power consumer and electric power consumer groups can be given. The load operation probability of an electric power consumer and electric power consumer groups can be given by calculations using algorithms.

Method for acquiring load operation probability of electric power consumer and method for acquiring load operation probability of electric power consumer groups

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

Yung-Chieh Hung