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Box-meter integrated solution for power data imputation through device design and deep learning integration.

Chen Gao¹, Hua Lin², Yinrong Lin¹

¹State Grid Fujian Marketing Service Center, Fuzhou, 350013, China.

Scientific Reports

|October 21, 2025

Summary

This summary is machine-generated.

Smart metering data loss is reduced with a new integrated device and deep learning imputation. TimesNet model shows best performance in recovering missing power data, enhancing analysis accuracy.

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Area of Science:

Electrical Engineering
Data Science
Artificial Intelligence

Background:

Smart metering systems face significant data loss due to sensor failures, communication issues, and maintenance.
Inaccurate power data hinders the reliability of intelligent scheduling and load forecasting in power systems.

Purpose of the Study:

To propose an integrated metering device with localized data imputation capabilities.
To evaluate the performance of deep learning models for power data imputation under various missing data rates.

Main Methods:

Development of a box-meter integrated device with analog signal acquisition and deep learning model deployment.
Comparative analysis of DLinear, TimesNet, and iTransformer deep learning models for data imputation.
Experimental evaluation under different data missing rates.

Main Results:

TimesNet demonstrated superior data imputation performance across all tested missing rates.
The integrated device effectively reduces data loss at the source, improving data continuity.
Enhanced data quality and real-time availability were achieved.

Conclusions:

The proposed integrated metering device combined with TimesNet offers an effective solution for smart metering data gaps.
This approach significantly improves data integrity for critical power system applications.
The system provides a robust foundation for advanced power analytics and grid management.