Anomaly detection method for energy storage system, power control system, and temperature prediction device

A power control system, comprising:

• a sensor module, configured to retrieve a plurality of sensing data of an electronic component, wherein each of the plurality of sensing data is respectively related to one type:
• a storage medium, connected to the sensor module and configured to store a temperature prediction model, wherein the temperature prediction model comprises:
• a plurality of model encoders, configured to receive the plurality of sensing data:
• a reweighting model, connected to the plurality of model encoders;
• and
• a model decoder, connected to the reweighting model;
• and
• a computation device, connected to the sensor module and the storage medium and configured to perform operations by using the temperature prediction model, comprising steps of:
• receiving, by each of the plurality of model encoders, the plurality of sensing data of same type and respectively computing a plurality of time-series features related to the type by using the plurality of sensing data of same type:
• outputting the plurality of time-series features of each type to the reweighting model:
• computing, by the reweighting model, a predicted temperature feature based on the plurality of time-series features and outputting the predicted temperature feature to the model decoder:
• reconstructing, by the model decoder, the predicted temperature feature to generate a predicted temperature of the electronic component;
• and
• determining whether the electronic component operates abnormally by estimating an error between the predicted temperature and a current temperature.

A temperature prediction device, comprising:

• a storage medium, configured to store program codes;
• and
• a processor, connected to the storage medium and configured to load the program codes to perform operations comprising steps of:
• receiving a plurality of sensing data retrieved from an electronic component of an energy storage system, wherein each of the plurality of sensing data is respectively related to one type;
• respectively inputting the plurality of sensing data to a temperature prediction model according to the type of each of the plurality of sensing data, wherein the temperature prediction model comprises a plurality of model encoders, a reweighting model, and a model decoder;
• receiving, by each of the plurality of model encoders, the plurality of sensing data of same type and respectively computing a plurality of time-series features related to the type based on the plurality of sensing data of same type;
• outputting the plurality of time-series features of each type to the reweighting model;
• computing, by the reweighting model, a predicted temperature feature by using the plurality of time-series features and outputting the predicted temperature feature to the model decoder;
• and
• reconstructing, by the model decoder, the predicted temperature feature to generate a predicted temperature of the electronic component.