Distribution Transformer Load Monitoring and Aging
This dataset provides granular, time-stamped monitoring of individual distribution transformer loads, including 15-minute demand curves, peak load, load factor, hotspot temperature, and insulation aging rates. It enables utilities and energy analysts to assess transformer health, optimize asset management, and predict maintenance needs based on operational and environmental conditions.
Sample rows
preview · 8 of 133 rows · all 14 columns| transformer_idstring | measurement_periodstring | load_factorfloat | location_countrystring | location_streetstring | location_citystring | location_statestring | location_postal_codestring | timestampdatetime | demand_kwfloat | peak_load_kwfloat | hotspot_temperature_cfloat | ambient_temperature_cfloat | insulation_aging_ratefloat |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| US-001-A | monthly | 0.98 | USA | 1205 Market St | San Francisco | CA | 94103 | 2024-06-14T08:00:00 | 980.5 | 1000 | 135.2 | 42.3 | 0.042 |
| CA-002-B | daily | 0.9 | Canada | 211 King St W | Toronto | ON | M5H1K5 | 2024-06-14T08:15:00 | 450.3 | 500 | 112.7 | 26.1 | 0.024 |
| DE-003-X | hourly | 0.8 | Germany | Leipziger Str 15 | Berlin | BE | 10117 | 2024-06-14T08:30:00 | 320.5 | 400 | 89 | 19.6 | 0.018 |
| FR-004-T | daily | 0.73 | France | 4 Rue du Commerce | Paris | IDF | 75015 | 2024-06-14T08:45:00 | 220 | 300 | 74.2 | 23.4 | 0.015 |
| JP-005-Y | monthly | 0.96 | Japan | 2-1-1 Marunouchi | Tokyo | Tokyo | 100-0005 | 2024-06-14T09:00:00 | 1150.3 | 1200 | 142.7 | 33.3 | 0.035 |
| US-006-C | 15min | 0.42 | USA | 599 West Ave | Chicago | IL | 60654 | 2024-06-14T09:15:00 | 38.2 | 90 | 64.9 | 11.2 | 0.007 |
| UK-007-P | hourly | 0.76 | UK | 221B Baker St | London | London | NW16XE | 2024-06-14T09:30:00 | 90.7 | 120 | 81.1 | 17.8 | 0.013 |
| BR-008-Q | monthly | 0.96 | Brazil | Rua Augusta 125 | São Paulo | SP | 01305-000 | 2024-06-14T09:45:00 | 670.6 | 700 | 123.8 | 38.1 | 0.028 |
| RU-009-V | monthly | 0.94 | Russia | 15 Tverskaya St | Moscow | Moscow | 125009 | 2024-06-14T10:00:00 | 845.4 | 900 | 129.1 | 23.7 | 0.034 |
| IN-010-W | hourly | 0.8 | India | Plot 22, Nehru Nagar | Mumbai | MH | 400017 | 2024-06-14T10:15:00 | 2490.7 | 3100 | 168.6 | 56.7 | 0.95 |
| ZA-011-L | 15min | 0 | South Africa | 16 Nelson Mandela Sq | Johannesburg | GP | 2196 | 2024-06-14T10:30:00 | 0 | 0 | -40 | -50 | 0 |
| MX-012-M | 15min | 0.11 | Mexico | blank | Monterrey | NL | 64000 | 2024-06-14T10:45:00 | 5.6 | 50 | 44.1 | 28.4 | 0.001 |
| NG-013-N | 15min | 0.13 | Nigeria | 12 Allen Ave | Lagos | LA | 100001 | 2024-06-14T11:00:00 | 1.3 | 10 | 48.5 | 34.2 | 0.002 |
| US-014-D | daily | 0.61 | USA | 1150 Broadway | New York | NY | 10010 | 2024-06-14T11:15:00 | 110.6 | 180 | 66.4 | 17.1 | 0.013 |
| AU-015-E | monthly | 0.89 | Australia | 37 Queen St | Brisbane | QLD | 4000 | 2024-06-14T11:30:00 | 1770.5 | 2000 | 154.2 | 52.5 | 0.051 |
| CN-016-F | 15min | 0.4 | China | 88 Nanjing Rd | Shanghai | SH | 200001 | 2024-06-14T11:45:00 | 0.2 | 0.5 | 45.6 | 13.8 | 0.001 |
| IT-017-G | daily | 0.89 | Italy | Via Roma 99 | Rome | RM | 00184 | 2024-06-14T12:00:00 | 250 | 280 | 104.1 | 29.6 | 0.022 |
| ES-018-H | hourly | 0.85 | Spain | Calle Mayor 20 | Madrid | MD | 28013 | 2024-06-14T12:15:00 | 340.6 | 400 | 96.8 | 24.7 | 0.019 |
| FR-019-I | monthly | 0.9 | France | 11 Boulevard St Michel | Paris | IDF | 75005 | 2024-06-14T12:30:00 | 630 | 700 | 121.2 | 37.1 | 0.025 |
| US-020-F | 15min | 0.26 | USA | 3801 Walnut St | Denver | CO | 80205 | 2024-06-14T12:45:00 | 52.1 | 200 | 61.2 | 12.4 | 0.006 |
What the 133 rows show
from the 133-row sample15min (measurement period) stands out: mean load_
- 0.85median load_
factor - 330.7median demand_
kw - 400.0median peak_
load_ kw - 87.3median hotspot_
temperature_ c - 28.7median ambient_
temperature_ c - 0.02median insulation_
aging_ rate
Median 0.85, from 0.0 to 1.0.
- string 7
- float 6
- datetime 1
Columns
14 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 7 columns | |||
transformer_id | string | Unique identifier for the distribution transformerunique | US-001-A |
location_street | string | Street address of the transformer locationoptional | 1205 Market St |
location_city | string | City where the transformer is locatedoptional | San Francisco |
location_state | string | State or region of the transformer locationoptional | CA |
location_postal_code | string | Postal code of the transformer locationoptional | 94103 |
location_country | string | Country where the transformer is locatedoptional | USA |
measurement_period | string | Period over which peak load and load factor are calculated (e.g., 'daily', 'monthly')15min · hourly · daily · monthly | monthly |
| Numbers 6 columns | |||
demand_kw | float | Measured load demand in kilowatts (kW) for the 15-minute interval0 or more | 980.5 |
peak_load_kw | float | Peak load in kilowatts (kW) observed during the measurement period0 or more | 1000 |
load_factor | float | Load factor calculated as average load divided by peak load (ratio between 0 and 1)0 to 1 | 0.98 |
hotspot_temperature_c | float | Calculated hotspot temperature of the transformer in degrees Celsius-40 to 200 | 135.2 |
ambient_temperature_c | float | Ambient temperature in degrees Celsius at the time of measurement-50 to 60 | 42.3 |
insulation_aging_rate | float | Calculated rate of insulation aging (per unit time, e.g., per hour or per day)0 or more | 0.042 |
| Dates and times 1 column | |||
timestamp | datetime | Timestamp for the 15-minute demand measurement | 2024-06-14T08:00:00 |
Use it for
An energy dashboard
Load_
factor by measurement_ period and a breakdown of location_ country. Excel, Power BI or Tableau. Why do the 23 15min rows have a mean load_
factor of 0.14? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Transformers133US-001-A0.98monthlyCA-002-B0.9dailyDE-003-X0.8hourly
A software demo
Believable transformers with location_
street, location_ city and location_ state to fill a screen in front of a buyer.
This dataset133 rows14 columns
Yours1,000 rows14 columnslocation_street: UK only
blueprint · distribution-transformer-load-monitoring-and-aging
Behind this dataset
Same schema. As many rows as you need.
These 133 rows came out of a blueprint — 14 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
- Transformer ID, rated kVA capacity, and installation date
- 15-minute kVA loading and percentage of rated capacity
- Peak load and time of peak occurrence
- Load factor = average load / peak load (typical 0.3-0.6)
- Hotspot temperature estimation (top-oil + gradient)
- IEEE C57.91 aging acceleration factor based on temperature
- Estimated remaining life in years
- Overload conditions and emergency rating (short-term)
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
- distribution-transformer-load-monitoring-and-aging