Energy
21 columns
120 rows
8 formats

Smart Building Energy Optimization

This dataset provides granular IoT sensor readings from smart buildings, including environmental, occupancy, and energy consumption data, along with spatial and operational context. It enables facility managers and analysts to identify energy waste, predict occupancy trends, automate system responses, and monitor ESG compliance, supporting advanced sustainability and efficiency strategies.

last updated 23 Nov 2025
by GoMask

The brief that made it

Energy usage monitoring and waste identification

Sample rows

preview · 8 of 120 rows · all 21 columns

reading_idstring	sensor_typestring	reading_valuefloat	anomaly_detectedboolean	building_namestring	building_idstring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	floorinteger	zonestring	sensor_idstring	sensor_modelstring	reading_timestampdatetime	reading_unitstring	occupancy_statusboolean	system_statusstring	energy_consumption_kwhfloat	esg_compliance_flagboolean
R00001-20240601T083012	temperature	21.2	false	Central Plaza	B001	123 Market St	New York	NY	10001	USA	2	Lobby	S00001-B001	TempSense A100	2024-06-01T08:30:12	Celsius	blank	HVAC_on	blank	true
R00002-20240601T093045	humidity	48.6	false	Central Plaza	B001	123 Market St	New York	NY	10001	USA	2	Lobby	S00002-B001	HumidX H200	2024-06-01T09:30:45	%	blank	HVAC_on	blank	true
R00003-20240601T101104	co2	512	false	Central Plaza	B001	123 Market St	New York	NY	10001	USA	5	Conference Room	S00003-B001	CO2Smart S300	2024-06-01T10:11:04	ppm	blank	HVAC_on	blank	false
R00004-20240601T113022	energy_meter	489.45	false	Tech Campus - Block A	B002	200 Innovation Ave	San Francisco	CA	94107	USA	1	Server Room	S00004-B002	EnergyPro E1500	2024-06-01T11:30:22	kWh	blank	HVAC_on	489.45	true
R00005-20240601T121507	light	1755	false	Tech Campus - Block A	B002	200 Innovation Ave	San Francisco	CA	94107	USA	3	Open Workspace	S00005-B002	LightLux L500	2024-06-01T12:15:07	lux	blank	lights_on	blank	false
R00006-20240601T133801	motion	1	false	Tech Campus - Block A	B002	200 Innovation Ave	San Francisco	CA	94107	USA	0	Parking	S00006-B002	MotionEye M800	2024-06-01T13:38:01	motion	blank	lights_on	blank	false
R00007-20240601T145112	energy_meter	1200.8	true	Northpoint Factory	B003	800 Industrial Rd	Cleveland	OH	44115	USA	0	Production Line	S00007-B003	EnergyPro E1500	2024-06-01T14:51:12	kWh	blank	other	1200.8	false
R00008-20240601T153529	co2	4580	true	Northpoint Factory	B003	800 Industrial Rd	Cleveland	OH	44115	USA	1	Rack Zone	S00008-B003	CO2Smart S300	2024-06-01T15:35:29	ppm	blank	HVAC_off	blank	true
R00009-20240601T162211	temperature	18.4	false	Seaside Resort	B004	17 Ocean Dr	Santa Monica	CA	90401	USA	10	Guest Room	S00009-B004	TempSense A100	2024-06-01T16:22:11	Celsius	blank	HVAC_off	blank	false
R00010-20240601T175829	humidity	56.8	false	Seaside Resort	B004	17 Ocean Dr	Santa Monica	CA	90401	USA	0	Lounge	S00010-B004	HumidX H200	2024-06-01T17:58:29	%	blank	HVAC_off	blank	false
R00011-20240601T182305	occupancy	1	false	Historic Library	B005	12 Beacon St	Boston	MA	02108	USA	1	Reading Hall	S00011-B005	OccuTrack O100	2024-06-01T18:23:05	occupied	true	lights_on	blank	true
R00012-20240601T194421	light	895	false	Historic Library	B005	12 Beacon St	Boston	MA	02108	USA	0	Main Entrance	S00012-B005	LightLux L500	2024-06-01T19:44:21	lux	blank	lights_on	blank	true
R00013-20240601T201930	humidity	72.1	false	Historic Library	B005	12 Beacon St	Boston	MA	02108	USA	2	Classroom	S00013-B005	HumidX H200	2024-06-01T20:19:30	%	blank	lights_off	blank	true
R00014-20240601T210522	temperature	20.5	false	Skyline Apartments	B006	99 Lake Shore Dr	Chicago	IL	60601	USA	15	Penthouse	S00014-B006	TempSense A100	2024-06-01T21:05:22	Celsius	blank	HVAC_on	blank	true
R00015-20240601T223140	motion	0	false	Skyline Apartments	B006	99 Lake Shore Dr	Chicago	IL	60601	USA	4	Lounge	S00015-B006	MotionEye M800	2024-06-01T22:31:40	motion	blank	lights_off	blank	true
R00016-20240601T233514	occupancy	0	false	Maple School	B007	77 Elm St	Toronto	ON	M4Y2A1	Canada	2	Classroom	S00016-B007	OccuTrack O100	2024-06-01T23:35:14	unoccupied	false	lights_off	blank	true
R00017-20240602T002131	light	1740	false	Maple School	B007	77 Elm St	Toronto	ON	M4Y2A1	Canada	0	Main Entrance	S00017-B007	LightLux L500	2024-06-02T00:21:31	lux	blank	lights_on	blank	false
R00018-20240602T011922	energy_meter	1425.6	true	Cloud Data Center	B008	255 Binary Blvd	Newark	NJ	07102	USA	0	Rack Zone	S00018-B008	EnergyPro E1500	2024-06-02T01:19:22	kWh	blank	HVAC_off	1425.6	false
R00019-20240602T023410	co2	9250	true	Cloud Data Center	B008	255 Binary Blvd	Newark	NJ	07102	USA	0	Storage	S00019-B008	CO2Smart S300	2024-06-02T02:34:10	ppm	blank	other	blank	true
R00020-20240602T033214	humidity	86.7	false	Sunshine Mall	B009	55 Commerce St	Dallas	TX	75201	USA	1	Main Entrance	S00020-B009	HumidX H200	2024-06-02T03:32:14	%	blank	HVAC_on	blank	true

What the 120 rows show

from the 120-row sample

Other (sensor type) stands out: 12 of its 12 rows have anomaly_detected = true, against 21 of 108 for the rest.

28%anomaly_detected = true
69.7median reading_value
5address countries
5system statuses
11reading units
15address states

Anomaly detected rate by sensor_typeanomaly_detected = true

reading_value120 rows, in bands of 10k

Median 69.7, from -100.5 to 49,999.

building_name119 rows with a value · 1 left blank

Cloud Data Center12
Sunshine Mall12
International HQ9
Tech Campus - Block C9
Skyline Apartments8
Midtown Office8
Northpoint Factory7
Maple School7
Historic Library6
Mountain Retreat6

21 columns by typefrom the column list below

string 14
integer 1
float 2
datetime 1
boolean 3

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 14 columns
`reading_id`	string	Unique identifier for each sensor reading recordunique	R00001-20240601T083012
`building_id`	string	Unique identifier for the building where the sensor is located	B001
`building_name`	string	Name of the buildingoptional	Central Plaza
`address_street`	string	Street address of the buildingoptional	123 Market St
`address_city`	string	City where the building is locatedoptional	New York
`address_state`	string	State or region of the buildingoptional	NY
`address_postal_code`	string	Postal code of the buildingoptional	10001
`address_country`	string	Country where the building is located5 countries · optional	USA
`zone`	string	Zone, room, or area within the building where the sensor is locatedoptional	Lobby
`sensor_id`	string	Unique identifier for the sensor device	S00001-B001
`sensor_type`	string	Type of sensor (e.g., temperature, humidity, occupancy, CO2, light, energy_meter, etc.)8 values	temperature
`sensor_model`	string	Model or manufacturer of the sensoroptional	TempSense A100
`reading_unit`	string	Unit of measurement for the reading (e.g., Celsius, %, ppm, lux, kWh, etc.)11 units	Celsius
`system_status`	string	Operational status of the building system at the time of reading (e.g., HVAC_on, HVAC_off, lights_on, lights_off, etc.)HVAC_on · HVAC_off · lights_on · lights_off · other · optional	HVAC_on
Numbers 3 columns
`floor`	integer	Floor number where the sensor is installed0 or more · optional	2
`reading_value`	float	Measured value from the sensor (unit depends on sensor_type)	21.2
`energy_consumption_kwh`	float	Energy consumed in kilowatt-hours at the time of reading (if applicable)0 or more · optional	489.45
Dates and times 1 column
`reading_timestamp`	datetime	Date and time when the sensor reading was captured	2024-06-01T08:30:12
True or false 3 columns
`occupancy_status`	boolean	Indicates if the area is occupied at the time of reading (true = occupied, false = unoccupied); relevant for occupancy sensorsoptional	true
`esg_compliance_flag`	boolean	Indicates whether the reading/event is part of an ESG compliance initiativeoptional	true
`anomaly_detected`	boolean	Indicates if an anomaly (e.g., abnormal consumption or sensor fault) was detected for this readingoptional	false

Use it for

An energy dashboard
The anomaly_detected rate, reading_value by sensor_type and a breakdown of building_name. Excel, Power BI or Tableau.
Why do 33 of 120 rows have anomaly_detected = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Readings120
R00001-20240601T08301221.2temperat…
R00007-20240601T1451121200.8energy_m…
R00008-20240601T1535294580co2
A software demo
Believable readings with building_id, building_name and address_street to fill a screen in front of a buyer.

This dataset120 rows21 columns

Yours1,000 rows21 columnsaddress_street: UK only

blueprint · smart-building-energy-optimization

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 21 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 records aggregated hourly sensor data per building zone
Data includes occupancy estimate, temperature, humidity, and energy use
Anomalies are flagged if energy use exceeds baseline by >15%
Occupancy predictions adjust HVAC and lighting settings automatically
Sensor failures are annotated for maintenance prioritization

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

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: smart-building-energy-optimization

What should your data show?

No signup. No card.

Sample rows

What the 120 rows show

Columns

Use it for

An energy dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Energy

What should your data show?