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AI-based Models for Detecting Measurement Deviations in Energy Meters (AIM)

AI-based Models for Detecting Measurement Deviations in Energy Meters (AIM)

The reliability of modern energy infrastructure hinges on the sustained metrological integrity of its metering instruments. However, energy meters (both heat and electricity) are susceptible to measurement drift—a critical systematic error characterized by a gradual, temporal deviation in accuracy from their initial calibration state due to environmental factors, component degradation, or operational stress.

Start date: 2024-05-01

End date: 2025-12-31

The primary objective of this project is to develop a robust, AI-based framework for the remote detection and alerting of these metrological measurement errors specifically arising from meter drift during the meter's operational lifespan. This remote analysis confirms the error's existence in-situ (in the meter) by leveraging consumption data transmitted to a central platform.

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Updated: 2025-11-03

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