Electric energy meter aging state prediction method and system based on LSTM
By constructing an aging driving force index in the prediction of the aging status of electricity meters, finding a matching vector and fine-tuning it, the problem of large error in the field application of LSTM model is solved, and more accurate aging status prediction is achieved.
CN122087464AActive Publication Date: 2026-05-26WUXI HENGTONG ELECTRIC CO LTD
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Patent Information
- Application Number
- CN202610561032.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-27
- Publication Date
- 2026-05-26
- Estimated Expiration
- 2046-04-27
AI Technical Summary
Technical Problem
Existing LSTM models have large prediction errors in predicting the aging status of electricity meters due to the mismatch between laboratory data and field data.
Method used
By constructing aging driving force indices of simulated vectors and field vectors, matching vectors are found and fine-tuned. The mapping vectors are then used to train the prediction model to reduce the model prediction error.
Benefits of technology
It effectively reduces the prediction error of the model in actual field applications, making the prediction results more accurately match the actual working conditions on site, and solving the problem of mismatch between laboratory data and field data.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of electric energy meters, in particular to an LSTM-based electric energy meter aging state prediction method and system, and the method comprises the steps: calculating the aging driving force index of each field vector and the aging driving force index of each simulation vector, taking any field vector as a target vector, and calculating the aging driving force index of each simulation vector; calculating an absolute difference value between the aging driving force index of the target vector and the aging driving force index of each simulation vector, and taking the simulation vector corresponding to the minimum value of the absolute difference value as a matching vector of the target vector, and combining the aging driving force index of the target vector, the aging driving force index of the matching vector and the matching vector to construct a mapping vector of the target vector, traversing to obtain the mapping vector of each field vector, performing fine adjustment on the prediction model according to all the mapping vectors, and performing aging state prediction on the to-be-predicted electric energy meter according to the fine-adjusted prediction model. According to the invention, the error of the LSTM model in actual prediction of the aging state of the electric energy meter can be reduced.
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