Energy Data
Energy Data Quality: Meter Validation and Missing Data Management
Energy analytics depends on trustworthy measurement data before model choice. Missing intervals, multiplier errors and clock shifts can silently distort totals, so data-quality checks should be automated and traceable.
Core quality checks
Flag missing, duplicate and out-of-order records against the expected interval. Define separate rules for negative consumption, values above physical capacity, frozen meters and discrepancies between main and submeters.
How should missing data be filled?
Short gaps may use neighbouring values or similar-day profiles; long outages require production schedules or backup meters. Every estimated value should retain its method and confidence level without overwriting the raw measurement.
Standardise time and units
UTC, local time and daylight-saving rules must be converted explicitly between sources. kW, kWh and cumulative meter indexes should not share the same semantics; unit and measurement type belong in the schema.
Sources
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