• Energy
  • 20 columns
  • 100 rows
  • 8 formats

AMI Tamper and Theft Detection Events

This dataset records detailed security events from smart meters, highlighting potential tampering, electricity theft, and abnormal consumption patterns. It includes event specifics, meter and customer identifiers, location data, consumption metrics, severity ratings, and resolution status, making it ideal for utility fraud detection, operational analytics, and regulatory reporting.

  • last updated 2 Nov 2025
  • by GoMask
The brief that made it

Electricity theft and tampering detection for utility companies

Sample rows

preview · 8 of 100 rows · all 20 columns
event_idstringmeter_statusstringseverity_levelintegertamper_flagbooleanlocation_statestringmeter_idstringcustomer_idstringevent_typestringevent_timestampdatetimelocation_street_addressstringlocation_citystringlocation_postal_codestringlocation_countrystringconsumption_before_eventfloatconsumption_after_eventfloattheft_flagbooleanresolved_flagbooleanresolution_timestampdatetimeevent_descriptionstringresolution_notesstring
EVT-00001error2trueILMTR-1001CST-501tampering2024-06-01T08:22:15Z1024 Maple AveSpringfield62701USA153.4153.4falsetrue2024-06-03T10:14:13ZMeter casing appeared forced open; physical tamper detected.Technician replaced meter; security seal updated.
EVT-00002active3trueOHMTR-1002CST-502meter_bypass2024-06-01T09:35:41Z88 Elm StCleveland44101USA210.745.3truefalseblankBypass detected; abnormal drop in usage.blank
EVT-00003active1falseOHMTR-1003CST-503reverse_energy_flow2024-06-01T10:15:22Z362 North StColumbus43215USA97.597.6falsetrue2024-06-02T11:40:05ZReverse energy flow detected; possible rooftop solar misconfiguration.Customer solar inverter reconfigured.
EVT-00004active2trueOHMTR-1004CST-504cover_removal2024-06-01T11:05:03Z14 Pine StDayton45402USA124.9124.9falsefalseblankMeter cover removed; possible inspection or unauthorized access.blank
EVT-00005error3trueINMTR-1005CST-505magnetic_interference2024-06-01T12:10:56Z700 Oakwood DrIndianapolis46201USA185.2185.2falsetrue2024-06-04T09:22:33ZDetected strong magnetic field near meter.Magnet removed; meter recalibrated.
EVT-00006active2falseOHMTR-1002CST-502unusual_consumption2024-06-02T01:02:18Z88 Elm StCleveland44101USA50.6145.1truefalseblankSudden spike in electricity usage during non-peak hours.blank
EVT-00007error2trueKYMTR-1006CST-506tampering2024-06-02T04:15:59Z320 Walnut StLexington40507USA78.378.3falsetrue2024-06-03T07:32:18ZSeal broken; evidence of tampering around meter base.Meter replaced and seal restored.
EVT-00008offline3trueKYMTR-1007CST-507meter_bypass2024-06-02T06:17:34Z19 Cherry LnLouisville40202USA112.622.1truefalseblankMeter bypass attempt detected; wiring anomaly found.blank

What the 100 rows show

from the 100-row sample

Active (meter status) stands out: mean severity_level is 1.6, against 2.8 for the rest.

  • 66%tamper_flag = true
  • 2median severity_level
  • 6event types
  • 79.7median consumption_before_event
  • 76.2median consumption_after_event
Mean severity_level by meter_status100 rows
0241.6active58 rows3.0offline8 rows2.7error34 rows
severity_level100 rows, in bands of 0.2
025502300004300034123severity_level →

Median 2, from 1 to 3.

location_state100 rows · top 10 of 28 values
  1. TX13
  2. IL11
  3. NV9
  4. KY8
  5. CO7
  6. CT7
  7. IA5
  8. OH4
  9. FL4
  10. TN3
20 columns by typefrom the column list below
  • string 12
  • integer 1
  • float 2
  • datetime 2
  • boolean 3

Columns

20 columns in four groups
blueprint · 20 columns
columntypedescriptionexample
Text 12 columns
event_idstringUnique identifier for each tamper or theft detection eventuniqueEVT-00001
meter_idstringUnique identifier for the smart meter reporting the eventMTR-1001
customer_idstringUnique identifier for the customer associated with the meterCST-501
event_typestringType of security event detected (e.g., tampering, bypass, reverse flow, cover removal, magnetic interference, unusual consumption)6 valuestampering
event_descriptionstringDetailed description of the event and any relevant observationsoptionalMeter box lock damaged.
location_street_addressstringStreet address where the meter is installedoptional1024 Maple Ave
location_citystringCity where the meter is installedoptionalSpringfield
location_statestringState or province where the meter is installedoptionalIL
location_postal_codestringPostal code for the meter installation locationoptional62701
location_countrystringCountry where the meter is installedoptionalUSA
meter_statusstringOperational status of the meter at the time of the event (e.g., active, offline, error)active · offline · error · optionalerror
resolution_notesstringNotes or comments regarding the resolution of the eventoptionalHoliday usage confirmed.
Numbers 3 columns
consumption_before_eventfloatRecorded electricity consumption (kWh) immediately before the event0 or more · optional153.4
consumption_after_eventfloatRecorded electricity consumption (kWh) immediately after the event0 or more · optional153.4
severity_levelintegerSeverity rating of the event (1=Low, 2=Medium, 3=High)1 to 3 · optional2
Dates and times 2 columns
event_timestampdatetimeDate and time when the event was detected2024-06-01T08:22:15Z
resolution_timestampdatetimeDate and time when the event was resolved (if applicable)optional2024-06-03T10:14:13Z
True or false 3 columns
tamper_flagbooleanIndicates if the event was classified as tamperingtrue
theft_flagbooleanIndicates if the event was classified as theftfalse
resolved_flagbooleanIndicates if the event has been investigated and resolvedtrue

Use it for

  • tamper flag66%66 of 100 rowsmean severity level b…1.6active3.0offline2.7error

    An energy dashboard

    The tamper_flag rate, severity_level by meter_status and a breakdown of location_state. Excel, Power BI or Tableau.

  • Why do the 58 active rows have a mean severity_level of 1.6?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable events with meter_id, customer_id and event_type to fill a screen in front of a buyer.

Not quite right?

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This dataset100 rows20 columns
Yours1,000 rows20 columnslocation_street_address: UK only

blueprint · ami-tamper-and-theft-detection-events

Behind this dataset

Same schema. As many rows as you need.

These 100 rows came out of a blueprint — 20 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Event type: cover removal, reverse flow, magnetic tamper, etc.
  • Event timestamp and meter ID
  • Abnormal consumption patterns (sudden drops, flat profiles)
  • Voltage imbalance or phase loss indicating bypass
  • Investigation status: pending, in progress, resolved
  • Estimated revenue loss from theft period
  • Field inspection required flag
  • Customer account flagged for collections or disconnect
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
ami-tamper-and-theft-detection-events

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