School IoT Energy Savings Tracker

This dataset enables education technology teams to monitor, analyze, and optimize energy consumption across school buildings using granular IoT sensor data. It supports anomaly detection, upgrade prioritization, and ESG/sustainability reporting, helping districts identify inefficiencies and drive data-driven facility improvements. The schema is ideal for operational analytics, sustainability tracking, and smart building management.

  • last updated 27 Nov 2025
  • by GoMask
The brief that made it

Real-time monitoring and optimization of school building energy usage

Sample rows

preview · 8 of 150 rows · all 26 columns
district_idstringsensor_typestringupgrade_priorityintegeris_anomalybooleandistrict_namestringschool_idstringschool_namestringbuilding_idstringbuilding_namestringaddress_streetstringaddress_citystringaddress_statestringaddress_postal_codestringaddress_countrystringsensor_idstringsensor_locationstringreading_idstringreading_datetimedatetimeenergy_consumption_kwhfloatgas_consumption_m3floatwater_consumption_litersfloattemperature_celsiusfloathumidity_percentfloatanomaly_typestringesg_reportedbooleanrecommended_actionstring
D001electricityblankfalseNorth Valley Unified DistrictS1001North Valley Elementary SchoolB1001Main Academic Building123 Maple LaneSpringfieldCA90001USAE1001Room 201, 2nd FloorR000012024-02-21T09:15:00312.6blankblankblankblanknonetrueblank
D002gas2trueEastside Education DistrictS1002Eastside Middle SchoolB1002Science Wing456 Oak StreetMadisonTX73301USAG1001Mechanical RoomR000022024-02-21T10:05:00blank148.3blankblankblankspiketrueInspect boiler for irregular gas usage.
D003water3trueGreenfield Public SchoolsS1003Greenfield High SchoolB1003Athletics Center789 Pine DriveGreenfieldFL33101USAW1001Locker Room AreaR000032024-02-21T11:10:00blankblank20899blankblankleaktrueConduct plumbing inspection for possible leaks.
D004temperatureblankfalseWestside Charter SchoolsS1004Westside Charter AcademyB1004Library Annex321 Cedar AveDenverCO80203USAT1001Reading Room 1R000042024-02-21T09:35:00blankblankblank18.7blanknonefalseblank
D005humidityblankfalseCentral Urban School DistrictS1005Central Urban High SchoolB1005Arts Building654 Elm StreetMinneapolisMN55401USAH1001Art Studio AR000052024-02-21T09:50:00blankblankblankblank61.4nonetrueblank
D006other1trueRiverbend Community SchoolsS1006Riverbend Elementary SchoolB1006Cafeteria234 Willow RoadRiverbendNY10003USAO1001Kitchen StorageR000062024-02-21T12:30:00blankblankblank-47.2blankotherfalseCalibrate temperature sensor and verify installation.
D007electricityblankfalseHillside Unified SchoolsS1007Hillside Middle SchoolB1007Gymnasium890 Birch BlvdHillsideUT84101USAE1002Main Gym Lighting PanelR000072024-02-21T08:45:00185.7blankblankblankblanknonetrueblank
D008gas2trueLakeshore Area SchoolsS1008Lakeshore Primary SchoolB1008Administration Wing555 Lakeview AveLakeshoreND58102USAG1002Furnace RoomR000082024-02-21T11:20:00blank0blankblankblankoutagetrueCheck gas supply and furnace operation.

What the 150 rows show

from the 150-row sample

Water (sensor type) stands out: mean upgrade_priority is 3.9, against 2.0 for the rest.

  • 38%is_anomaly = true
  • 2median upgrade_priority
  • 6anomaly types
  • 22address states
  • 227.6median energy_consumption_kwh
  • 301.2median gas_consumption_m3
Mean upgrade_priority by sensor_type57 rows
0241.4elect…7 rows1.8gas13 rows3.9water12 rows1.3tempe…4 rows3.4humid…5 rows2.1other16 rows
upgrade_priority57 rows, in bands of 0.5
010201701909066135upgrade_priority →

Median 2, from 1 to 5.

district_id150 rows · top 10 of 35 values
  1. D01911
  2. D01810
  3. D0209
  4. D0217
  5. D0226
  6. D0236
  7. D0246
  8. D0256
  9. D0276
  10. D0286
26 columns by typefrom the column list below
  • string 17
  • integer 1
  • float 5
  • datetime 1
  • boolean 2

Columns

26 columns in four groups
blueprint · 26 columns
columntypedescriptionexample
Text 17 columns
district_idstringUnique identifier for the school districtD001
district_namestringName of the school districtWestside Charter Schools
school_idstringUnique identifier for the schooluniqueS1001
school_namestringName of the schoolEastside Middle School
building_idstringUnique identifier for the building within the schooluniqueB1001
building_namestringName or description of the buildingMain Academic Building
address_streetstringStreet address of the building123 Maple Lane
address_citystringCity where the building is locatedSpringfield
address_statestringState where the building is locatedCA
address_postal_codestringPostal code for the building address90001
address_countrystringCountry of the building addressUSA
sensor_idstringUnique identifier for the IoT sensoruniqueE1001
sensor_typestringType of IoT sensor (e.g., electricity, gas, water, temperature, humidity)6 valueselectricity
sensor_locationstringLocation within the building where the sensor is installed (e.g., room number, floor)Room 201, 2nd Floor
reading_idstringUnique identifier for the sensor readinguniqueR00001
anomaly_typestringType of anomaly detected (e.g., spike, outage, leak, overuse)6 values · optionalnone
recommended_actionstringSuggested action to address detected inefficiency or anomalyoptionalInspect boiler for irregu…
Numbers 6 columns
energy_consumption_kwhfloatMeasured energy consumption in kilowatt-hours (kWh) for the reading period0 or more · optional312.6
gas_consumption_m3floatMeasured gas consumption in cubic meters (m³) for the reading period0 or more · optional148.3
water_consumption_litersfloatMeasured water consumption in liters for the reading period0 or more · optional20899
temperature_celsiusfloatMeasured temperature in Celsius at the time of reading-50 to 60 · optional18.7
humidity_percentfloatMeasured humidity as a percentage at the time of reading0 to 100 · optional61.4
upgrade_priorityintegerPriority level for facility upgrade based on detected inefficiency (1=highest, 5=lowest)1 to 5 · optional2
Dates and times 1 column
reading_datetimedatetimeTimestamp when the sensor reading was recorded2024-02-21T09:15:00
True or false 2 columns
is_anomalybooleanIndicates if the reading is flagged as an anomaly or inefficiencyoptionalfalse
esg_reportedbooleanIndicates if the data point was included in ESG/sustainability reportingoptionaltrue

Use it for

  • is anomaly38%57 of 150 rowsmean upgrade priority…1.4elec…1.8gas3.9water1.3temp…

    An education dashboard

    The is_anomaly rate, upgrade_priority by sensor_type and a breakdown of district_id. Excel, Power BI or Tableau.

  • Why do the 12 water rows have a mean upgrade_priority of 3.9?

    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 districts with district_name, school_id and school_name to fill a screen in front of a buyer.

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This dataset150 rows26 columns
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blueprint · school-iot-energy-savings-tracker

Behind this dataset

Same schema. As many rows as you need.

These 150 rows came out of a blueprint — 26 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 row represents daily aggregated readings from a unique school building IoT energy sensor.
  • Include metrics for electricity, heating, and cooling usage (kWh, BTUs).
  • Flag readings that exceed defined efficiency thresholds.
  • Provide contextual data: day of week, weather impact factor, and occupancy status.
  • Include calculated daily CO2 emissions for ESG tracking.
  • Sensor uptime must be logged; gaps indicate maintenance needs.
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
school-iot-energy-savings-tracker

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