Real Estate
15 columns
180 rows
8 formats

Building IoT Sensor Efficiency Logs

This dataset provides granular, real-time operational logs from IoT sensors deployed in commercial buildings, capturing sensor readings, location, status, and maintenance indicators. It enables proptech teams to monitor equipment efficiency, detect anomalies, and optimize resource usage for sustainability and improved tenant experience. The dataset is ideal for predictive maintenance, energy management, and smart building analytics.

last updated 7 Dec 2025
by GoMask

The brief that made it

Real-time monitoring of building sensor efficiency

Sample rows

preview · 8 of 180 rows · all 15 columns

log_idstring	statusstring	sensor_valuefloat	anomaly_detectedboolean	sensor_typestring	sensor_idstring	building_idstring	floor_numberinteger	room_numberstring	timestampdatetime	unitstring	battery_levelfloat	signal_strengthfloat	maintenance_flagboolean	notesstring
LOG-000001	active	22.7	false	temperature	TEMP-X92A	BLDG-01	1	101	2024-06-24T07:10:23Z	°C	82.5	-41.2	false	Routine morning temperature check.
LOG-000002	active	45.3	false	humidity	HUM-21B7	BLDG-02	2	Lobby	2024-06-24T08:15:00Z	%	74.1	-55.3	false	Humidity within expected range for lobby.
LOG-000003	active	17	false	occupancy	OCC-8F12	BLDG-03	0	Reception	2024-06-24T09:00:05Z	count	69.8	-36.7	false	Reception area occupancy spike at shift change.
LOG-000004	active	128.76	false	energy	ENRG-5178	BLDG-04	5	510A	2024-06-24T09:21:14Z	kWh	91.2	-38.4	false	Morning HVAC energy consumption within target.
LOG-000005	active	785	false	air_quality	AIR-9C33	BLDG-01	3	303	2024-06-24T10:05:45Z	ppm	87	-43.1	false	CO₂ levels normal for occupied office.
LOG-000006	active	9	false	motion	MOT-74XZ	BLDG-05	0	Entrance	2024-06-24T10:20:00Z	count	79.9	-51	false	Low motion detected at entrance during quiet hour.
LOG-000007	active	25.1	false	temperature	TEMP-Q1FT	BLDG-02	4	405	2024-06-24T10:45:11Z	°C	78.3	-42.9	false	Temperature stable after AC adjustment.
LOG-000008	active	42	false	occupancy	OCC-2J6M	BLDG-06	6	603B	2024-06-24T11:00:00Z	count	73.6	-39.7	false	Meeting room occupancy at scheduled time.
LOG-000009	active	57.2	false	humidity	HUM-3L99	BLDG-03	1	104B	2024-06-24T11:36:21Z	%	88.7	-47.6	false	Humidity slightly above recommended for archive room.
LOG-000010	active	112.05	false	energy	ENRG-8492	BLDG-04	4	401	2024-06-24T12:00:00Z	kWh	96.4	-37.8	false	Elevator energy consumption within normal limits.
LOG-000011	active	630	false	air_quality	AIR-4K7T	BLDG-01	2	202	2024-06-24T12:20:42Z	ppm	84.5	-44.9	false	CO₂ levels slightly elevated after lunch.
LOG-000012	active	23.9	false	temperature	TEMP-Z6UQ	BLDG-05	2	201	2024-06-24T12:35:00Z	°C	80.6	-45.2	false	Temperature check after maintenance completed.
LOG-000013	active	49.8	false	humidity	HUM-8XKR	BLDG-02	5	504	2024-06-24T13:00:00Z	%	76.7	-48.5	false	Routine humidity reading for server room.
LOG-000014	active	15	false	occupancy	OCC-1K4Y	BLDG-03	1	Lobby	2024-06-24T13:22:09Z	count	71.5	-39.2	false	Low occupancy during lunch break.
LOG-000015	active	94.33	false	energy	ENRG-3321	BLDG-06	3	302	2024-06-24T13:45:00Z	kWh	94.1	-40.5	false	Lighting system energy use within target.
LOG-000016	active	520	false	air_quality	AIR-5M8C	BLDG-02	0	001	2024-06-24T14:05:37Z	ppm	88.6	-46.4	false	Basement air quality normal.
LOG-000017	active	21	false	motion	MOT-9WQ2	BLDG-07	1	102	2024-06-24T14:30:00Z	count	85.7	-41.7	false	High motion detected near stairwell during lunch.
LOG-000018	active	24.2	false	temperature	TEMP-59KJ	BLDG-03	0	001	2024-06-24T15:00:00Z	°C	81	-47.1	false	Lobby temperature consistent with prior readings.
LOG-000019	active	25	false	occupancy	OCC-7R8E	BLDG-05	2	202	2024-06-24T15:15:00Z	count	77.2	-39.6	false	Conference room occupancy at scheduled event time.
LOG-000020	active	37.9	false	humidity	HUM-49XP	BLDG-07	3	301	2024-06-24T15:32:54Z	%	83.2	-44.5	false	Humidity check for top floor storage.

What the 180 rows show

from the 180-row sample

13%anomaly_detected = true
30.6median sensor_value
6units
49buildings
83.8median battery_level
-44.3median signal_strength

status180 rows by value

sensor_value180 rows, in bands of 20k

Median 30.6, from -16.2 to 100,000.

sensor_type180 rows · 7 values

temperature36
energy32
humidity31
occupancy31
air_quality31
motion15
other4

15 columns by typefrom the column list below

string 8
integer 1
float 3
datetime 1
boolean 2

Columns

15 columns in four groups

blueprint · 15 columns
column	type	description	example
Text 8 columns
`log_id`	string	Unique identifier for each sensor efficiency log entryunique	LOG-000001
`sensor_id`	string	Unique identifier for the IoT sensor reporting the log	TEMP-X92A
`sensor_type`	string	Type of IoT sensor (e.g., temperature, humidity, occupancy, energy)7 values	temperature
`building_id`	string	Unique identifier for the building where the sensor is located	BLDG-01
`room_number`	string	Room or zone identifier within the buildingoptional	101
`unit`	string	Unit of measurement for the sensor value (e.g., °C, %, kWh, ppm)7 values	°C
`status`	string	Operational status of the sensor at the time of loggingactive · inactive · error · maintenance	active
`notes`	string	Additional remarks or comments regarding the log entryoptional	Normal office occupancy.
Numbers 4 columns
`floor_number`	integer	Floor number within the building where the sensor is installed0 or more	1
`sensor_value`	float	Measured value reported by the sensor (e.g., temperature in Celsius, energy in kWh)	22.7
`battery_level`	float	Battery level of the sensor as a percentage (0-100)0 to 100 · optional	82.5
`signal_strength`	float	Wireless signal strength of the sensor (e.g., dBm)optional	-41.2
Dates and times 1 column
`timestamp`	datetime	Date and time when the log entry was recorded	2024-06-24T07:10:23Z
True or false 2 columns
`anomaly_detected`	boolean	Indicates if an anomaly or abnormal reading was detectedoptional	false
`maintenance_flag`	boolean	Flag indicating if the sensor requires maintenanceoptional	false

Use it for

A real estate dashboard
The anomaly_detected rate, sensor_value by status and a breakdown of sensor_type. Excel, Power BI or Tableau.
Why do 23 of 180 rows have anomaly_detected = true?
A class exercise
Hand out the rows and one question. Everyone works from the same 180 rows.
Logs180
LOG-00000122.7active
LOG-0000671830.2error
LOG-00006898.7error
A software demo
Believable logs with sensor_id, sensor_type and building_id to fill a screen in front of a buyer.

This dataset180 rows15 columns

Yours1,000 rows15 columns

blueprint · building-iot-sensor-efficiency-logs

Behind this dataset

Same schema. As many rows as you need.

These 180 rows came out of a blueprint — 15 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 one sensor event with timestamp and device ID
Resource consumption values are normalized per building zone
Anomaly flags indicate threshold breaches for temperature, humidity, or occupancy
Device status (active/inactive/error) must align with manufacturer specifications
Events with simultaneous occupancy and high resource use require review

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

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: building-iot-sensor-efficiency-logs

What should your data show?

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Sample rows

What the 180 rows show

Columns

Use it for

A real estate dashboard

A class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Real Estate

What should your data show?