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

In smart metering systems, data loss often occurs due to sensor failures, communication delays, and equipment maintenance, affecting the accuracy of power data analysis. This study proposes an box-meter integrated metering device that supports localized data imputation and combines it with deep learning models for further research. We compared the imputation performance of different deep learning models-including DLinear, TimesNet, and iTransformer-under varying missing rates. Experimental results show that TimesNet achieves optimal imputation performance across diverse missing scenarios. The device is capable of deploying deep learning models and integrates a raw analog signal acquisition interface, thereby reducing data loss at the source and enhancing data continuity and real-time availability. This approach improves data quality and timeliness, providing a solid data foundation for power system tasks such as intelligent scheduling and load forecasting.