AI System for Checking Energy Meter Data Quality

The problem

Missing or duplicate meter data may distort reports in day-to-day operations. When meter identity, timestamp, index, multiplier, connection and maintenance records are scattered across sensor, document, site or team records, the issue may not be identified in time.

This fragmented data delays preparation of the meter data quality report and forces teams to search for supporting evidence again by hand. Before making a decision, the metering or energy engineer must complete missing records and verify each inconsistency individually.

A common exception is that a meter replacement or index reset may create a valid jump. Incomplete or incorrect automation that does not check this context could apply an incorrect consumption correction or affect billing, so no physical or operational action can be taken directly.

What we set out to improve

• Number of data quality events confirmed by the metering engineer
• Proportion of outputs corrected or rejected by the metering or energy engineer
• Time between the source event and the review output

How the system works

1. 01 Define the review scope and the role of the authorised metering or energy engineer
2. 02 Retrieve the inputs: meter identity, timestamp, index, multiplier, connection and maintenance records
3. 03 Check the source identifier, date, calibration or version, and data recency
4. 04 Check identity, sequence and time integrity
5. 05 Compare jumps with maintenance events
6. 06 Check the exception: a meter replacement or index reset may create a valid jump
7. 07 Place low-confidence or inconsistent records in a separate specialist queue
8. 08 Write the source identifier, rule, output and time to the audit trail
9. 09 Have the metering or energy engineer approve, correct, defer or reject the output

Methods we used

• Time-series integrity and meter-status checks
• Source, format, time and required-field checks for meter identity, timestamp, index, multiplier, connection and maintenance records
• A missing-data warning instead of an estimate when inputs are incomplete or inconsistent
• A separate exception check outside the main rule
• Joint retention of the source, rule result, meter data quality report and human decision

Where people stay involved

Data and security

Meter identity, timestamp, index, multiplier, connection and maintenance records are accessed with the narrowest permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary portion of the data is sent to the model rather than the complete raw dataset. Access, outputs and the metering or energy engineer's decision are recorded; production does not begin until the organisation approves the retention period and data location.

Who this suits

Frequently asked questions