Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Scientific Reports

DOI: 10.1038/s41598-025-18439-3

Authors: Chen Gao, Hua Lin, Yinrong Lin

Institution: State Grid Corporation of China (China)

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,690