Energy
21 columns
120 rows
8 formats

Smart Utility Consumption Tracker

This dataset provides granular, time-stamped IoT-based readings of electricity, water, and gas consumption across diverse assets, including detailed location, cost, and sustainability metrics. It enables benchmarking, anomaly detection, and supports green initiatives through actionable data for operations and sustainability teams. The structure is optimized for analytics, reporting, and integration with energy management platforms.

last updated 19 Dec 2025
by GoMask

The brief that made it

Benchmarking utility efficiency across multiple assets

Sample rows

preview · 8 of 120 rows · all 21 columns

reading_idstring	reading_statusstring	consumption_valuefloat	anomaly_flagboolean	location_statestring	asset_idstring	asset_namestring	asset_typestring	location_street_addressstring	location_citystring	location_postal_codestring	location_countrystring	meter_idstring	meter_typestring	reading_timestampdatetime	consumption_unitstring	anomaly_reasonstring	co2_estimate_kgfloat	cost_estimatefloat	tariff_codestring	notesstring
R0001	valid	1386.4	false	IL	A-101	Main Office	building	123 Oak Ave	Chicago	60601	USA	M1001	electricity	2024-06-01T10:23:00	kWh	blank	832.28	210.2	T-A	Normal working day
R0002	valid	4129.2	false	TX	A-102	Warehouse A	building	456 Maple St	Houston	77002	USA	M1002	water	2024-06-01T14:45:00	L	blank	13.52	36.53	T-W3	Peak usage period
R0003	valid	0	false	AZ	A-103	Solar Array East	equipment	789 Elm Rd	Phoenix	85001	USA	M1003	electricity	2024-06-01T13:05:00	kWh	blank	0	0	T-SOL	Solar generation
R0004	estimated	384.2	false	TX	A-104	Production Line 1	equipment	321 Pine Blvd	Dallas	75201	USA	M1004	gas	2024-06-01T04:15:00	therm	blank	2101.1	159.7	T-G1	Standby period
R0005	valid	54.8	false	blank	A-105	Reception Area	floor	654 Cedar Ave	Berlin	10115	Germany	M1005	water	2024-06-01T09:27:00	m3	blank	0.28	9.36	T-W2	Regular cleaning
R0006	valid	68.9	false	blank	A-106	Cold Storage Unit	zone	987 Spruce St	London	EC1A 1BB	UK	M1006	gas	2024-06-01T21:32:00	therm	blank	376.76	28.21	T-G3	Night shift operation
R0007	valid	178.1	false	blank	A-107	Cafeteria	floor	321 Willow Dr	Paris	75001	France	M1007	water	2024-06-01T12:10:00	L	blank	0.21	2.13	T-W4	Lunch period
R0008	valid	2906.7	false	VIC	A-108	HVAC Compressor	equipment	111 Birch Pl	Melbourne	3000	Australia	M1008	electricity	2024-06-01T16:50:00	kWh	blank	1744.02	434.32	T-DC	Afternoon cooling
R0009	missing	0	false	blank	A-109	Quality Control Lab	other	222 Aspen Ct	Madrid	28001	Spain	M1009	water	2024-06-01T08:20:00	gal	blank	blank	blank	blank	Idle period
R0010	estimated	842.8	false	CA	A-110	Server Room	equipment	333 Cypress Rd	San Jose	95110	USA	M1010	electricity	2024-06-01T01:00:00	kWh	blank	506.16	128.52	T-B	Nighttime operation
R0011	valid	128.3	false	FL	A-111	Main Lobby	building	444 Chestnut St	Miami	33101	USA	M1011	electricity	2024-06-01T07:44:00	kWh	blank	77.06	19.55	T-A	Early morning
R0012	valid	21.6	false	BC	A-112	Zone 5 North	zone	555 Alder Ln	Vancouver	V5K 0A1	Canada	M1012	gas	2024-06-01T17:05:00	therm	blank	118.13	8.97	T-G2	Short cycle
R0013	valid	0.3	false	blank	A-113	Maintenance Bay	other	666 Redwood St	Dublin	D02 AF30	Ireland	M1013	gas	2024-06-01T02:54:00	therm	blank	1.64	0.13	T-G3	Minimum detectable flow
R0014	valid	457.9	false	ON	A-114	Floor 2 South	floor	777 Magnolia Ave	Toronto	M5H 2N2	Canada	M1014	electricity	2024-06-01T11:36:00	kWh	blank	275.37	69.83	T-A	Midday shift
R0015	valid	271.1	false	NSW	A-115	Boiler Room	equipment	888 Poplar St	Sydney	2000	Australia	M1015	gas	2024-06-01T18:22:00	therm	blank	1483.68	120.39	T-G1	End of day run
R0016	estimated	0	false	MI	A-116	Battery Storage	equipment	1010 Walnut St	Detroit	48201	USA	M1016	electricity	2024-06-01T11:01:00	kWh	blank	0	0	T-B	Idle
R0017	missing	0	false	blank	A-117	Zone B East	zone	1110 Fir St	Marseille	13001	France	M1017	water	2024-06-01T22:41:00	L	blank	blank	blank	blank	Vacant
R0018	valid	142.3	false	WA	A-118	Lighting Control Panel	equipment	1212 Palm Ave	Seattle	98101	USA	M1018	electricity	2024-06-01T03:27:00	kWh	blank	85.56	21.68	T-DC	Night lighting
R0019	valid	8712.2	false	FL	A-119	Main Warehouse	building	1313 Maple St	Orlando	32801	USA	M1019	water	2024-06-01T15:29:00	L	blank	28.54	57.66	T-W1	Monthly peak
R0020	missing	0	false	blank	A-120	blank	other	1414 Pine St	Frankfurt	60311	Germany	M1020	gas	2024-06-01T19:13:00	therm	blank	blank	blank	blank	Test cycle

What the 120 rows show

from the 120-row sample

Anomaly_detected (reading status) stands out: mean consumption_value is 7,558, against 738.6 for the rest.

11%anomaly_flag = true
179.2median consumption_value
3meter types
5asset types
5consumption units
8location countries

Mean consumption_value by reading_status120 rows

consumption_value120 rows, in bands of 5,000

Median 179.2, from 0.0 to 25,500.

location_state78 rows with a value · 42 left blank

TX10
ON8
CA7
NSW7
IL6
WA6
VIC5
FL5
QLD5
AZ4

21 columns by typefrom the column list below

string 16
float 3
datetime 1
boolean 1

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 16 columns
`reading_id`	string	Unique identifier for each utility reading recordunique	R0001
`asset_id`	string	Unique identifier for the asset (building, facility, or equipment) being monitored	A-101
`asset_name`	string	Descriptive name of the asset (e.g., Main Office, Warehouse A)optional	Main Office
`asset_type`	string	Type of asset being monitored (e.g., building, equipment, floor)building · equipment · floor · zone · other · optional	building
`location_street_address`	string	Street address of the asset locationoptional	123 Oak Ave
`location_city`	string	City where the asset is locatedoptional	Chicago
`location_state`	string	State or province where the asset is locatedoptional	IL
`location_postal_code`	string	Postal or ZIP code of the asset locationoptional	60601
`location_country`	string	Country where the asset is located8 countries · optional	USA
`meter_id`	string	Unique identifier for the IoT meter or sensor	M1001
`meter_type`	string	Type of utility meter (electricity, water, gas)electricity · water · gas	electricity
`consumption_unit`	string	Unit of measurement for the consumption value (e.g., kWh, m3, L)6 values	kWh
`reading_status`	string	Status of the reading (valid, estimated, missing, anomaly_detected)valid · estimated · missing · anomaly_detected · optional	valid
`anomaly_reason`	string	Description or code for the anomaly reason, if flaggedoptional	Possible leak detected
`tariff_code`	string	Code or identifier for the tariff or rate applied to this readingoptional	T-A
`notes`	string	Additional notes or comments about the readingoptional	Normal working day
Numbers 3 columns
`consumption_value`	float	Measured consumption value for the utility (e.g., kWh, m3, L)0 or more	1386.4
`co2_estimate_kg`	float	Estimated CO2 emissions in kilograms associated with the consumption (if applicable)0 or more · optional	832.28
`cost_estimate`	float	Estimated cost of the consumption for the reading period (in local currency)0 or more · optional	210.2
Dates and times 1 column
`reading_timestamp`	datetime	Date and time when the reading was recorded	2024-06-01T10:23:00
True or false 1 column
`anomaly_flag`	boolean	Indicates if the reading is flagged as an anomalyoptional	false

Use it for

An energy dashboard
The anomaly_flag rate, consumption_value by reading_status and a breakdown of location_state. Excel, Power BI or Tableau.
Why do the 13 anomaly_detected rows have a mean consumption_value of 7,558?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Readings120
R00011386.4valid
R002110532.9anomaly_…
R00282176.3anomaly_…
A software demo
Believable readings with asset_id, asset_name and asset_type to fill a screen in front of a buyer.

This dataset120 rows21 columns

Yours1,000 rows21 columnslocation_street_address: UK only

blueprint · smart-utility-consumption-tracker

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 asset records electricity, water, and gas consumption daily via IoT sensors.
Flag readings 20% above average as 'anomaly' for investigation.
Calculate total and average consumption per asset monthly.
Tag assets meeting ENERGY STAR benchmarks as 'efficient'.
Include timestamps and sensor status for each reading.

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-utility-consumption-tracker

What should your data show?

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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?