IoT Home Energy Patterns
This dataset provides high-frequency, household-level IoT sensor readings of energy consumption, power, voltage, and current, along with contextual information such as appliance type, room location, occupancy, and environmental conditions. It enables detailed analysis of residential energy patterns for smart grid optimization, demand forecasting, and personalized energy-saving recommendations.
Sample rows
preview · 8 of 200 rows · all 16 columns| household_idstring | household_typestring | household_sizeinteger | is_occupiedboolean | appliance_typestring | sensor_idstring | sensor_typestring | timestampdatetime | energy_consumed_kwhfloat | power_wattsfloat | voltage_voltsfloat | current_amperesfloat | room_locationstring | outdoor_temperature_cfloat | outdoor_humidity_percentfloat | household_zip_codestring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HH001 | detached_house | 4 | blank | blank | SM1001 | smart_meter | 2023-01-10T05:23:00Z | 3.45 | 1380 | 238.3 | 5.79 | blank | -12.5 | 42.1 | 10012 |
| HH002 | apartment | 2 | true | refrigerator | APM2001 | appliance_monitor | 2023-03-15T18:50:00Z | 0.35 | 870 | 120.4 | 7.23 | kitchen | 17.9 | 56.8 | 60605 |
| HH003 | apartment | 3 | true | lighting | PLG3001 | plug | 2023-06-05T13:04:00Z | 0.01 | 13 | 119.6 | 0.11 | bedroom | 28.4 | 49.2 | 77019 |
| HH004 | detached_house | 6 | true | blank | THM4001 | thermostat | 2022-12-22T07:23:00Z | 0.12 | 40 | 24.3 | 1.65 | living_room | -3.5 | 62.4 | 19103 |
| HH005 | other | 10 | blank | blank | OTH5001 | other | 2022-07-18T15:44:00Z | 0 | 0 | 0 | 0 | blank | 25.2 | 71.5 | 90210 |
| HH006 | apartment | 2 | blank | blank | SM1002 | smart_meter | 2022-10-03T02:12:00Z | 0.98 | 400 | 230.3 | 1.74 | blank | 18.7 | 54 | 02139 |
| HH007 | apartment | 2 | false | dishwasher | PLG3002 | plug | 2022-04-21T12:32:00Z | 0.04 | 91 | 119.8 | 0.76 | kitchen | 8.9 | 35.4 | 38103 |
| HH008 | detached_house | 5 | true | HVAC | APM2002 | appliance_monitor | 2023-02-11T19:40:00Z | 3.89 | 1331 | 230.8 | 5.77 | living_room | 1 | 31.8 | 75201 |
| HH009 | townhouse | 3 | false | blank | THM4002 | thermostat | 2023-05-12T09:02:00Z | 0.07 | 20 | 24.1 | 0.83 | bedroom | 24.6 | 42.3 | 30309 |
| HH010 | detached_house | 5 | blank | blank | SM1003 | smart_meter | 2023-08-19T21:19:00Z | 8.2 | 4300 | 241 | 17.84 | blank | 36.7 | 64.5 | 98109 |
| HH011 | townhouse | 2 | true | lighting | PLG3003 | plug | 2023-09-01T14:18:00Z | 0.13 | 99 | 120 | 0.82 | hallway | 27.3 | 53.2 | 19104 |
| HH012 | other | 7 | blank | blank | OTH5002 | other | 2023-11-16T03:40:00Z | 0.03 | 13 | 119.9 | 0.11 | garage | 7.8 | 81.1 | 60614 |
| HH013 | apartment | 3 | true | oven | APM2003 | appliance_monitor | 2023-04-08T17:21:00Z | 0.96 | 230 | 121.6 | 1.89 | kitchen | 22.3 | 58.7 | 94110 |
| HH014 | detached_house | 5 | false | EV_charger | APM2004 | appliance_monitor | 2022-09-28T22:55:00Z | 4.85 | 1500 | 240 | 6.25 | garage | 17.1 | 38.2 | 73099 |
| HH015 | townhouse | 2 | true | blank | THM4003 | thermostat | 2023-01-13T08:56:00Z | 0.09 | 35 | 24 | 1.46 | bathroom | -6.2 | 51.7 | 85016 |
| HH016 | other | 8 | blank | blank | SM1004 | smart_meter | 2023-03-29T23:03:00Z | 13.2 | 5400 | 232.2 | 23.26 | blank | 31 | 45 | 10011 |
| HH017 | apartment | 3 | true | lighting | PLG3004 | plug | 2023-10-01T16:25:00Z | 0.27 | 145 | 120.5 | 1.2 | living_room | 30.5 | 40.2 | 10001 |
| HH018 | apartment | 2 | false | microwave | APM2005 | appliance_monitor | 2023-12-11T20:45:00Z | 0.15 | 104 | 120.6 | 0.86 | kitchen | 5.1 | 29.7 | 80202 |
| HH019 | other | 1 | blank | blank | OTH5003 | other | 2022-06-06T16:33:00Z | 0 | 0 | 0 | 0 | blank | 41.4 | 12.5 | 10453 |
| HH020 | detached_house | 4 | blank | blank | SM1005 | smart_meter | 2023-07-23T16:57:00Z | 2.87 | 1389 | 239.2 | 5.81 | blank | 33.6 | 58.9 | 75001 |
What the 200 rows show
from the 200-row sampleOther (household type) stands out: mean household_
- 64%is_
occupied = true - 3median household_
size - 5sensor types
- 18room locations
- 0.19median energy_
consumed_ kwh - 76.0median power_
watts
Median 3, from 1 to 10.
- string 7
- integer 1
- float 6
- datetime 1
- boolean 1
Columns
16 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 7 columns | |||
household_id | string | Unique identifier for each household where IoT sensors are installed | HH001 |
sensor_id | string | Unique identifier for each IoT sensor device within a household | SM1001 |
sensor_type | string | Type of sensor (e.g., smart_meter, plug, thermostat, appliance_monitor)smart_meter · plug · thermostat · appliance_monitor · other | smart_meter |
appliance_type | string | Type of appliance or circuit being monitored (e.g., HVAC, refrigerator, lighting, general, unknown)optional | refrigerator |
room_location | string | Room or area within the household where the sensor is located (e.g., kitchen, living_room, bedroom, garage)optional | kitchen |
household_zip_code | string | Postal code of the household for regional analysisoptional | 10012 |
household_type | string | Type of household (e.g., apartment, detached_house, townhouse, other)apartment · detached_house · townhouse · other · optional | detached_house |
| Numbers 7 columns | |||
energy_consumed_kwh | float | Amount of energy consumed during the measurement interval, in kilowatt-hours (kWh)0 or more | 3.45 |
power_watts | float | Instantaneous power usage in watts at the time of measurement0 or more · optional | 1380 |
voltage_volts | float | Measured voltage in volts at the sensor during the reading0 or more · optional | 238.3 |
current_amperes | float | Measured electrical current in amperes at the sensor during the reading0 or more · optional | 5.79 |
outdoor_temperature_c | float | Outdoor temperature in degrees Celsius at the time of measurement (if available from sensors or external data)optional | -12.5 |
outdoor_humidity_percent | float | Outdoor relative humidity percentage at the time of measurement (if available)0 to 100 · optional | 42.1 |
household_size | integer | Number of people living in the household (if available)1 or more · optional | 4 |
| Dates and times 1 column | |||
timestamp | datetime | Date and time when the energy usage measurement was recorded | 2023-01-10T05:23:00Z |
| True or false 1 column | |||
is_occupied | boolean | Indicates if the room or household was occupied at the time of measurement (if available from sensors)optional | true |
Use it for
An energy dashboard
The is_
occupied rate, household_ size by household_ type and a breakdown of appliance_ type. Excel, Power BI or Tableau. Why do the 35 other rows have a mean household_
size of 8.6? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Households200HH0022apartmentHH0033apartmentHH0072apartment
A software demo
Believable households with sensor_
id, sensor_ type and timestamp to fill a screen in front of a buyer.
This dataset200 rows16 columns
Yours1,000 rows16 columnsroom_location: UK only
blueprint · iot-home-energy-patterns
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 16 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
- Smart meters sample every 15 minutes
- Households have varied appliance profiles
- Seasonal and daily usage trends evident
- Outage events reflected in missing data
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- iot-home-energy-patterns