Daily Power Consumption Records
This dataset provides detailed daily electricity consumption records for individual households, including total and segmented usage, peak demand, solar generation, and basic household demographics and location. It enables in-depth analysis for energy efficiency programs, demand forecasting, and targeted sustainability initiatives.
Sample rows
preview · 8 of 120 rows · all 17 columns| record_idstring | building_typestring | solar_generation_kwhfloat | has_solar_panelsboolean | statestring | household_idstring | record_datedate | total_consumption_kwhfloat | peak_consumption_kwhfloat | offpeak_consumption_kwhfloat | average_power_kwfloat | max_power_kwfloat | min_power_kwfloat | num_occupantsinteger | postal_codestring | citystring | countrystring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| REC00001 | detached_house | 10.2 | true | CA | HH001 | 2024-01-02 | 27.3 | 18.5 | 8.3 | 1.14 | 4.9 | 0.3 | 6 | 90077 | Beverly Hills | USA |
| REC00002 | apartment | 0 | false | NY | HH002 | 2024-02-29 | 8.5 | 3.6 | 4.8 | 0.35 | 1.7 | 0.1 | 2 | 10001 | New York | USA |
| REC00003 | semi_detached | 0 | false | ON | HH003 | 2023-12-15 | 19.8 | 13.1 | 6.6 | 0.83 | 3.2 | 0.2 | 4 | L4W3T9 | Toronto | Canada |
| REC00004 | townhouse | 5.8 | true | West Bengal | HH004 | 2024-01-10 | 21.4 | 14.9 | 6.3 | 0.89 | 3.7 | 0.3 | 5 | 700001 | Kolkata | India |
| REC00005 | apartment | 0 | false | Greater London | HH005 | 2024-03-01 | 5.7 | 1.6 | 4 | 0.24 | 1.1 | 0.1 | 1 | SW1A1AA | London | UK |
| REC00006 | detached_house | 15.9 | true | Karnataka | HH006 | 2024-01-22 | 34.7 | 24.4 | 10.2 | 1.45 | 6.8 | 0.5 | 7 | 560034 | Bangalore | India |
| REC00007 | apartment | 0 | false | Community of Madrid | HH007 | 2024-02-19 | 10.2 | 3.9 | 6.1 | 0.43 | 2.1 | 0.1 | 3 | 28001 | Madrid | Spain |
| REC00008 | semi_detached | 6.4 | true | CA | HH008 | 2024-01-18 | 16.6 | 9.8 | 6.5 | 0.69 | 2.8 | 0.2 | 4 | 94117 | San Francisco | USA |
| REC00009 | other | 0 | false | Noland | HH009 | 2024-02-01 | 42.7 | 27.8 | 14.2 | 1.78 | 7.2 | 0.7 | 10 | 00000 | Largeville | Noland |
| REC00010 | other | 0 | false | Noland | HH010 | 2024-01-27 | 0 | 0 | 0 | 0 | 0 | 0 | blank | 99999 | Ghosttown | Unknown |
| REC00011 | apartment | 0 | false | Île-de-France | HH011 | 2024-01-09 | 11.4 | 4.9 | 6.1 | 0.48 | 2.2 | 0.1 | 2 | 75001 | Paris | France |
| REC00012 | detached_house | 7.1 | true | Karnataka | HH012 | 2023-11-20 | 21.9 | 13.8 | 7.8 | 0.91 | 4.1 | 0.4 | 5 | 560002 | Bangalore | India |
| REC00013 | townhouse | 0 | false | Greater London | HH013 | 2024-02-07 | 15.9 | 10.2 | 5.6 | 0.66 | 2.7 | 0.2 | 4 | E14 5AB | London | UK |
| REC00014 | apartment | 0 | false | Delhi | HH014 | 2024-01-03 | 8.9 | 2.5 | 6.2 | 0.37 | 1.6 | 0.1 | 2 | 110001 | New Delhi | India |
| REC00015 | semi_detached | 6.8 | true | BC | HH015 | 2024-02-15 | 17.3 | 11.8 | 5.2 | 0.72 | 3.3 | 0.2 | 5 | V6E2E9 | Vancouver | Canada |
| REC00016 | apartment | 0 | false | Uttar Pradesh | HH016 | 2024-01-11 | 6.3 | 2 | 4.1 | 0.26 | 1.4 | 0.1 | 1 | 201301 | Noida | India |
| REC00017 | detached_house | 13.7 | true | CA | HH017 | 2024-01-24 | 31.2 | 20.1 | 10.7 | 1.3 | 5.8 | 0.4 | 6 | 92130 | San Diego | USA |
| REC00018 | townhouse | 0 | false | Maharashtra | HH018 | 2024-01-16 | 23.4 | 15 | 8.1 | 0.97 | 4.3 | 0.3 | 5 | 400013 | Mumbai | India |
| REC00019 | townhouse | 0 | false | CA | HH019 | 2024-01-20 | 12.2 | 7.1 | 5 | 0.51 | 2.4 | 0.2 | 3 | 94103 | San Francisco | USA |
| REC00020 | detached_house | 19.7 | true | Karnataka | HH020 | 2024-01-26 | 44.8 | 27.6 | 16.9 | 1.87 | 8.1 | 0.8 | 12 | 560037 | Bangalore | India |
What the 120 rows show
from the 120-row sampleDetached_
- 43%has_
solar_ panels = true - 0.0median solar_
generation_ kwh - 10countries
- 16.3median total_
consumption_ kwh - 9.6median peak_
consumption_ kwh - 6.3median offpeak_
consumption_ kwh
Every row with solar_
- string 7
- integer 1
- float 7
- date 1
- boolean 1
Columns
17 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 7 columns | |||
record_id | string | Unique identifier for each daily power consumption recordunique | REC00001 |
household_id | string | Unique identifier for each household | HH001 |
building_type | string | Type of building (e.g., apartment, detached house, townhouse)5 values · optional | detached_house |
postal_code | string | Postal code of the household's locationoptional | 90077 |
city | string | City where the household is locatedoptional | Beverly Hills |
state | string | State or province where the household is locatedoptional | CA |
country | string | Country where the household is located10 countries · optional | USA |
| Numbers 8 columns | |||
total_consumption_kwh | float | Total electricity consumed by the household on the given date, measured in kilowatt-hours (kWh)0 or more | 27.3 |
peak_consumption_kwh | float | Electricity consumed during peak hours, measured in kilowatt-hours (kWh)0 or more · optional | 18.5 |
offpeak_consumption_kwh | float | Electricity consumed during off-peak hours, measured in kilowatt-hours (kWh)0 or more · optional | 8.3 |
average_power_kw | float | Average power usage in kilowatts (kW) over the 24-hour period0 or more · optional | 1.14 |
max_power_kw | float | Maximum power draw (in kilowatts) recorded during the day0 or more · optional | 4.9 |
min_power_kw | float | Minimum power draw (in kilowatts) recorded during the day0 or more · optional | 0.3 |
num_occupants | integer | Number of people living in the household1 or more · optional | 6 |
solar_generation_kwh | float | Amount of electricity generated by solar panels on the given date, measured in kilowatt-hours (kWh)0 or more · optional | 10.2 |
| Dates and times 1 column | |||
record_date | date | Date for which the power consumption is recorded | 2024-01-02 |
| True or false 1 column | |||
has_solar_panels | boolean | Indicates if the household has solar panels installedoptional | true |
Use it for
An energy dashboard
The has_
solar_ panels rate, solar_ generation_ kwh by building_ type and a breakdown of state. Excel, Power BI or Tableau. Why do the 28 detached_
house rows have a mean solar_ generation_ kwh of 11.3? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Records120REC0000110.2detached…REC000020apartmentREC000045.8townhouse
A software demo
Believable records with household_
id, record_ date and total_ consumption_ kwh to fill a screen in front of a buyer.
blueprint · daily-power-consumption-records
Behind this dataset
Same schema. As many rows as you need.
These 120 rows came out of a blueprint — 17 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.
- Log consumption in kWh per household
- Capture peak usage time
- Flag households exceeding baseline by >20%
- Include day of week and weather conditions
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- daily-power-consumption-records