Energy
20 columns
72 rows
8 formats

Building IoT Energy Anomaly Log

This dataset logs energy usage anomalies detected by IoT sensors in smart buildings, including sensor details, anomaly classification, severity, timestamps, and resolution status. It empowers facilities managers and proptech architects to monitor, investigate, and resolve energy inefficiencies, supporting sustainability initiatives and operational optimization. The rich, structured data enables advanced analytics, fault detection, and compliance reporting for modern building management.

last updated 12 Jan 2026
by GoMask

The brief that made it

Automated detection and tracking of energy usage anomalies for smart buildings

Sample rows

preview · 8 of 72 rows · all 20 columns

anomaly_idstring	anomaly_typestring	anomaly_duration_minutesinteger	reported_bystring	building_idstring	building_namestring	sensor_idstring	sensor_typestring	location_floorstring	location_zonestring	anomaly_severitystring	anomaly_detected_atdatetime	energy_usage_valuefloat	expected_usage_valuefloat	usage_unitstring	resolution_statusstring	resolution_timestampdatetime	resolution_notesstring	environmental_impact_scorefloat	tagsstring
ANOM-1A4B-202406A1	spike	40	system_auto_detect	BLDG-CHIC-001	Loop Tower	SENS-HVAC-101	hvac_sensor	10	East Wing	high	2024-06-04T09:12:43	145.6	97.5	kWh	in_progress	blank	HVAC control recalibration scheduled.	39.2	HVAC,sustainability,spike
ANOM-2B3C-202406A2	drop	22	energy_manager	BLDG-NYC-002	Hudson Plaza	SENS-SMTR-202	smart_meter	Ground	Lobby	medium	2024-06-03T18:24:11	12.3	42.8	kWh	open	blank	blank	5.7	smart_meter,drop,energy
ANOM-3C4D-202406A3	sustained	120	system_auto_detect	BLDG-SF-003	Market Center	SENS-LGHT-303	lighting_sensor	3	Conference Hall	critical	2024-06-01T07:50:29	320.9	210.4	kWh	resolved	2024-06-01T09:12:30	Lighting system replaced.	65.1	lighting,sustainability,critical
ANOM-4D5E-202406A4	pattern_deviation	7	maintenance_team	BLDG-CHIC-001	Loop Tower	SENS-TEMP-104	temperature_sensor	10	Server Room	low	2024-06-05T12:18:10	48.2	52	Wh	false_positive	2024-06-05T12:35:22	Sensor calibration error.	1.7	temperature,sensor,low
ANOM-5E6F-202406A5	spike	51	system_auto_detect	BLDG-NYC-002	Hudson Plaza	SENS-HVAC-107	hvac_sensor	7	West Wing	medium	2024-06-04T14:02:44	179.8	110.2	kWh	open	blank	blank	47.8	HVAC,energy,medium
ANOM-6F7G-202406A6	other	3	building_operator	BLDG-LA-004	Sunset Offices	SENS-OTHR-401	other	2	Reception	low	2024-06-02T11:35:21	8.2	14.7	Wh	open	blank	blank	0.3	reception,low,other
ANOM-7G8H-202406A7	spike	115	system_auto_detect	BLDG-SF-003	Market Center	SENS-SMTR-306	smart_meter	12	Executive Suite	critical	2024-06-06T08:43:19	410.5	260.2	kWh	resolved	2024-06-06T11:20:09	Power surge isolated.	87.4	smart_meter,critical,spike
ANOM-8H9I-202406A8	pattern_deviation	18	facility_manager	BLDG-LA-004	Sunset Offices	SENS-LGHT-415	lighting_sensor	5	Atrium	medium	2024-06-03T16:24:08	63.4	57.8	Wh	in_progress	blank	Lighting schedule review ongoing.	2.8	lighting,medium,review
ANOM-9I0J-202406A9	sustained	90	maintenance_team	BLDG-CHIC-001	Loop Tower	SENS-HVAC-109	hvac_sensor	14	South Wing	high	2024-06-01T15:23:34	260.7	190.5	kWh	resolved	2024-06-01T16:55:10	Air filter replaced.	54.8	HVAC,sustainability,high
ANOM-0J1K-202406B1	drop	12	facility_manager	BLDG-NYC-002	Hudson Plaza	SENS-LGHT-210	lighting_sensor	4	North Wing	low	2024-06-05T19:20:44	27.9	41.2	kWh	false_positive	2024-06-05T19:40:21	Temporary blackout.	1.2	lighting,low,drop
ANOM-1K2L-202406B2	pattern_deviation	24	system_auto_detect	BLDG-LA-004	Sunset Offices	SENS-TEMP-417	temperature_sensor	3	Main Hall	medium	2024-06-03T09:13:25	39.7	45.2	Wh	open	blank	blank	1.8	temperature,sustainability,medium
ANOM-2L3M-202406B3	spike	180	energy_manager	BLDG-SF-003	Market Center	SENS-HVAC-315	hvac_sensor	17	Executive Lounge	critical	2024-06-07T13:07:40	501.2	330.6	kWh	resolved	2024-06-07T15:55:09	HVAC compressor failure replaced.	102.9	HVAC,critical,spike
ANOM-3M4N-202406B4	other	6	maintenance_team	BLDG-NYC-002	Hudson Plaza	SENS-OTHR-217	other	Basement	Storage	medium	2024-06-02T20:41:33	19.1	20	Wh	in_progress	blank	Awaiting part replacement.	0.6	storage,other,medium
ANOM-4N5O-202406B5	drop	5	system_auto_detect	BLDG-SF-003	Market Center	SENS-SMTR-309	smart_meter	8	Meeting Room 2	low	2024-06-05T04:51:28	6.7	19.5	kWh	resolved	2024-06-05T05:12:10	Scheduled maintenance.	0.4	smart_meter,low,drop
ANOM-5O6P-202406B6	sustained	70	facility_manager	BLDG-NYC-002	Hudson Plaza	SENS-HVAC-222	hvac_sensor	15	Mechanical Room	high	2024-06-02T12:13:57	380.2	250	kWh	in_progress	blank	Investigating HVAC valve.	76.5	HVAC,high,sustained
ANOM-6P7Q-202406B7	spike	29	building_operator	BLDG-LA-004	Sunset Offices	SENS-LGHT-423	lighting_sensor	1	East Entrance	medium	2024-06-07T06:24:18	101.4	70.2	kWh	open	blank	blank	18.1	lighting,spike,medium
ANOM-7Q8R-202406B8	pattern_deviation	14	system_auto_detect	BLDG-CHIC-001	Loop Tower	SENS-TEMP-113	temperature_sensor	12	Conference Room	low	2024-06-03T13:15:20	59.3	61.1	Wh	open	blank	blank	1.1	temperature,pattern_deviation,low
ANOM-8R9S-202406B9	other	62	facility_manager	BLDG-NYC-002	Hudson Plaza	SENS-OTHR-225	other	6	Security Office	critical	2024-06-06T21:03:50	51.8	20.7	Wh	resolved	2024-06-06T22:10:42	Short circuit repaired.	12.6	security,critical,other
ANOM-9S0T-202406C1	sustained	61	energy_manager	BLDG-SF-003	Market Center	SENS-HVAC-318	hvac_sensor	15	South Wing	high	2024-06-02T13:25:31	312.8	220.5	kWh	in_progress	blank	Part ordered for replacement.	57.2	HVAC,sustained,high
ANOM-0T1U-202406C2	drop	8	system_auto_detect	BLDG-LA-004	Sunset Offices	SENS-LGHT-429	lighting_sensor	6	South Entrance	medium	2024-06-05T21:13:42	11.9	30.5	kWh	false_positive	2024-06-05T21:28:00	Weather-related.	0.5	lighting,drop,weather

What the 72 rows show

from the 72-row sample

Sustained (anomaly type) stands out: mean anomaly_duration_minutes is 175.1, against 39.6 for the rest.

37.5median anomaly_duration_minutes
3usage units
4anomaly severities
4resolution statuses
5sensor types
15buildings

Mean anomaly_duration_minutes by anomaly_type72 rows

anomaly_duration_minutes72 rows, in bands of 50

Median 37.5, from 2 to 388.

reported_by72 rows · top 10 of 26 values

system_auto_detect12
energy_manager7
AutoDetectAI6
facility_manager5
building_ops5
maintenance_team4
facility_mgr4
building_operator3
jlee3
temp_monitor2

20 columns by typefrom the column list below

string 14
integer 1
float 3
datetime 2

Columns

20 columns in three groups

blueprint · 20 columns
column	type	description	example
Text 14 columns
`anomaly_id`	string	Unique identifier for the detected energy anomaly eventunique	ANOM-1A4B-202406A1
`building_id`	string	Unique identifier for the building where the anomaly was detected	BLDG-CHIC-001
`building_name`	string	Human-readable name of the building	Loop Tower
`sensor_id`	string	Unique identifier for the IoT sensor that detected the anomaly	SENS-HVAC-101
`sensor_type`	string	Type of IoT sensor (e.g., smart meter, HVAC sensor, lighting sensor)5 values	hvac_sensor
`location_floor`	string	Floor within the building where the sensor is locatedoptional	10
`location_zone`	string	Zone or area within the building (e.g., west wing, conference room)optional	East Wing
`anomaly_type`	string	Classification of the anomaly (e.g., spike, drop, sustained abnormal usage)spike · drop · sustained · pattern_deviation · other	spike
`anomaly_severity`	string	Severity level of the anomaly (e.g., low, medium, high, critical)low · medium · high · critical	high
`usage_unit`	string	Unit of measurement for energy usage (e.g., kWh, MWh)kWh · MWh · Wh	kWh
`resolution_status`	string	Current status of the anomaly resolution processopen · in_progress · resolved · false_positive	in_progress
`resolution_notes`	string	Notes or comments regarding the resolution of the anomalyoptional	Power surge isolated.
`reported_by`	string	Name or identifier of the person/system who reported or confirmed the anomalyoptional	system_auto_detect
`tags`	string	Comma-separated tags for anomaly categorization (e.g., sustainability, HVAC, lighting)optional	smart_meter,drop,energy
Numbers 4 columns
`energy_usage_value`	float	Measured energy usage value at the time of anomaly (e.g., kWh)0 or more	145.6
`expected_usage_value`	float	Expected or baseline energy usage value for comparison0 or more · optional	97.5
`anomaly_duration_minutes`	integer	Duration of the anomaly in minutes0 or more · optional	40
`environmental_impact_score`	float	Estimated environmental impact score of the anomaly (e.g., CO2 equivalent)0 or more · optional	39.2
Dates and times 2 columns
`anomaly_detected_at`	datetime	Timestamp when the anomaly was detected	2024-06-04T09:12:43
`resolution_timestamp`	datetime	Timestamp when the anomaly was resolved (if applicable)optional	2024-06-01T09:12:30

Use it for

An energy dashboard
Anomaly_duration_minutes by anomaly_type and a breakdown of reported_by. Excel, Power BI or Tableau.
Why do the 14 sustained rows have a mean anomaly_duration_minutes of 175.1?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Anomalies72
ANOM-1A4B-202406A140spike
ANOM-2B3C-202406A222drop
ANOM-3C4D-202406A3120sustained
A software demo
Believable anomalies with building_id, building_name and sensor_id to fill a screen in front of a buyer.

This dataset72 rows20 columns

Yours1,000 rows20 columnslocation_floor: UK only

blueprint · building-iot-energy-anomaly-log

Behind this dataset

Same schema. As many rows as you need.

These 72 rows came out of a blueprint — 20 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 a unique anomaly event flagged by IoT devices
Anomaly type must be categorized (e.g., overconsumption, device failure, unauthorized access)
Include device location, timestamp, and affected energy zone
Severity level classified as low, medium, or high
Resolution status tracked (open, investigating, resolved)

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-energy-anomaly-log

What should your data show?

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Sample rows

What the 72 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?