IoT Smart Appliance Usage Patterns

This dataset provides comprehensive event-level logs from smart IoT appliances, including usage metrics, sensor readings, error codes, and location data. It enables manufacturers and analysts to understand user behavior, optimize product features, and implement predictive maintenance strategies for connected devices.

  • last updated 21 Aug 2025
  • by GoMask
The brief that made it

Product development and feature optimization for smart appliances

Sample rows

preview · 8 of 200 rows · all 20 columns
event_idstringevent_typestringsensor_data_humidity_percentfloatmaintenance_requiredbooleanlocation_postal_codestringappliance_idstringappliance_typestringmodel_numberstringfirmware_versionstringuser_idstringevent_timestampdatetimeevent_descriptionstringerror_codestringusage_duration_secondsintegerenergy_consumed_kwhfloatlocation_street_addressstringlocation_citystringlocation_statestringlocation_countrystringsensor_data_temperature_cfloat
EVT-001-ABCD1234cycle_start44.9false53703APL-WM-0001washing_machineWMX-90003.2.1USR-10012024-03-05T07:24:11ZWashing cycle started: quick washblank18000.5515 Maple AvenueMadisonWIUSA38.2
EVT-002-4GHK8291power_on36.4false98103APL-OV-0004ovenOVN-44215.6.0USR-10072024-03-05T10:45:29ZOven powered on for bakingblankblankblank709 Cedar St.SeattleWAUSA23.1
EVT-003-KJH89123setting_change52.7false77004APL-RF-0003refrigeratorRFJ-21002.8.2USR-10132024-03-05T11:34:50ZTemperature set to 2°Cblankblankblank88 Market RoadHoustonTXUSA2
EVT-004-JJQW8932cycle_end67.3false92103APL-DW-0002dishwasherDWX-12001.5.7USR-10052024-03-05T13:15:19ZDishwasher completed normal cycleblank42001.1203 Spruce LaneSan DiegoCAUSA50
EVT-005-PLMN5632error12.3true85018APL-AC-0001air_conditionerACD-3304.1.0USR-10102024-03-05T14:02:30ZCompressor temperature too highE-42blankblank176 Oak CirclePhoenixAZUSA80.6
EVT-006-4LKM8721cycle_end45.5false53703APL-WM-0001washing_machineWMX-90003.2.1USR-10012024-03-05T15:01:47ZQuick wash cycle finishedblank18000.5215 Maple AvenueMadisonWIUSA36.8
EVT-007-9QW87219cycle_start33.7false53703APL-DR-0003dryerDRY-9012.0.0USR-10022024-03-05T15:34:18ZDryer started: mixed loadblank36001.844 Pine StreetMadisonWIUSA42.6
EVT-008-MNBV2145maintenance62.8true92103APL-DW-0002dishwasherDWX-12001.5.7USR-10052024-03-05T16:14:02ZFilter cleaning requiredblankblankblank203 Spruce LaneSan DiegoCAUSA45

What the 200 rows show

from the 200-row sample

Error (event type) stands out: 20 of its 20 rows have maintenance_required = true, against 14 of 180 for the rest.

  • 17%maintenance_required = true
  • 44.3median sensor_data_humidity_percent
  • 7appliance types
  • 15location states
  • 29location cities
  • 53firmware versions
Maintenance required rate by event_typemaintenance_required = true
0%50%100%0%powe…0 of …0%powe…0 of …0%cycl…0 of …0%cycl…0 of …100%error20 of…58%main…14 of…0%sett…0 of …
sensor_data_humidity_percent193 rows, in bands of 10
02550215843483236720050100sensor_data_humidity_percent →

Median 44.3, from 3.1 to 88.8.

location_postal_code200 rows · top 10 of 39 values
  1. 7520112
  2. 8030210
  3. 537039
  4. 277057
  5. 271057
  6. 278347
  7. 981036
  8. 331016
  9. 606016
  10. 276096
20 columns by typefrom the column list below
  • string 14
  • integer 1
  • float 3
  • datetime 1
  • boolean 1

Columns

20 columns in four groups
blueprint · 20 columns
columntypedescriptionexample
Text 14 columns
event_idstringUnique identifier for each appliance event log entryuniqueEVT-001-ABCD1234
appliance_idstringUnique identifier for the smart applianceAPL-WM-0001
appliance_typestringType of appliance (e.g., washing_machine, refrigerator, oven)7 valueswashing_machine
model_numberstringManufacturer's model number for the applianceWMX-9000
firmware_versionstringCurrent firmware version installed on the applianceoptional3.2.1
user_idstringUnique identifier for the user operating the applianceUSR-1001
event_typestringType of event (e.g., power_on, power_off, cycle_start, cycle_end, error, maintenance)8 valuescycle_start
event_descriptionstringDetailed description of the event or log entryoptionalTemperature set to 2°C
error_codestringError code reported by the appliance (if applicable)optionalE-42
location_street_addressstringStreet address where the appliance is locatedoptional15 Maple Avenue
location_citystringCity where the appliance is locatedoptionalMadison
location_statestringState or region where the appliance is locatedoptionalWI
location_postal_codestringPostal code of the appliance locationoptional53703
location_countrystringCountry where the appliance is locatedoptionalUSA
Numbers 4 columns
usage_duration_secondsintegerDuration of appliance usage in seconds for the event (if applicable)0 or more · optional1800
energy_consumed_kwhfloatAmount of energy consumed during the event (in kWh)0 or more · optional0.55
sensor_data_temperature_cfloatTemperature reading from the appliance sensor (Celsius)optional38.2
sensor_data_humidity_percentfloatHumidity reading from the appliance sensor (percentage)0 to 100 · optional44.9
Dates and times 1 column
event_timestampdatetimeDate and time when the event occurred2024-03-05T07:24:11Z
True or false 1 column
maintenance_requiredbooleanIndicates if maintenance is required after this eventoptionalfalse

Use it for

  • maintenance re…17%34 of 200 rowsmean sensor data humi…46.7powe…48.1powe…43.8cycl…45.4cycl…

    A technology dashboard

    The maintenance_required rate, sensor_data_humidity_percent by event_type and a breakdown of location_postal_code. Excel, Power BI or Tableau.

  • Why do 34 of 200 rows have maintenance_required = true?

    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 appliance_id, appliance_type and model_number to fill a screen in front of a buyer.

Not quite right?

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This dataset200 rows20 columns
Yours1,000 rows20 columnslocation_street_address: UK only

blueprint · iot-smart-appliance-usage-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 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
  • Each log includes appliance type, usage duration
  • Event types standardized
  • Outliers flagged for abnormal use
  • Error events logged separately
  • Device IDs anonymized
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
iot-smart-appliance-usage-patterns

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