Abstract

Operations sampling inspections, manual verification, user applications, and other labor-intensive methods are commonly used for energy meter error analysis in the power industry. Accurate fault detection in electric energy meters is crucial for achieving reliable measurements. The fundamental issue with on-site testing of electrical energy metering equipment is the characteristics of electric power meters under dynamic settings. This research develops a deep learning-assisted prediction model (DLPM) to address the problem of inaccurate energy power meters. Electricity is measured precisely, and the meters can pinpoint the most consequential deviations between the predicted and actual trajectories. The results of this research point to the widespread adoption of a consistent and autonomous method for analyzing discrepancies in energy meters. Compared to the traditional way, this technology considerably improves the electric intelligent meter deviance computation, providing exact data assistance for analysis and diagnostics of the source of the electric smart meter abnormality. The simulation results show that the suggested DLPM model has better prediction accuracy (99.2%), performance (97.8%), efficiency (98.9%), average consumption (10.3%), and root mean square error (11.2%) than the state-of-the-art.