Energy
13 columns
200 rows
8 formats

Smart Meter Consumption Patterns

This dataset provides high-frequency energy consumption readings from smart meters, including timestamps, location, customer identifiers, and sustainability metrics. It enables detailed analysis of peak usage patterns, geographic consumption trends, and renewable energy adoption, supporting utility optimization and research into energy efficiency.

last updated 5 Sept 2025
by GoMask

The brief that made it

Analyzing household and business energy consumption patterns

Sample rows

preview · 8 of 200 rows · all 13 columns

meter_idstring	tariff_codestring	renewable_energy_percentagefloat	peak_usage_flagboolean	meter_location_statestring	customer_idstring	timestampdatetime	consumption_kwhfloat	interval_minutesinteger	meter_location_streetstring	meter_location_citystring	meter_location_postal_codestring	meter_location_countrystring
MTR001	TOU_DAY	32.5	false	IL	CUST_AB123	2024-06-10T07:15:00Z	2.15	15	124 Maple Ave	Chicago	60614	USA
MTR002	TOU_EVENING	45.2	true	TX	CUST_XY789	2024-06-10T18:00:00Z	5.86	30	782 Oak Street	Dallas	75201	USA
MTR003	BUS_PEAK	10	true	TX	BUSS_CORP1	2024-06-10T12:00:00Z	120	180	900 Main Blvd	Houston	77002	USA
MTR004	NIGHT_RATE	78.1	false	WA	CUST_LM456	2024-06-09T23:59:00Z	0.23	5	337 6th St	Seattle	98101	USA
MTR005	HOTEL_PREMIUM	6.5	true	FL	HOTEL_PLAZA	2024-06-10T02:00:00Z	2100.5	1440	22 Ocean Dr	Miami	33139	USA
MTR006	BASIC	54.3	false	CO	CUST_VW321	2024-06-10T16:30:00Z	3.72	60	48 Elm Street	Denver	80203	USA
MTR007	TOU_DAY	29.8	false	MA	CUST_JK555	2024-06-10T09:45:00Z	0.85	10	16 Birch Rd	Boston	02108	USA
MTR008	INDUSTRIAL	14.2	true	ON	INDUSTRIAL_NOVA	2024-06-10T14:00:00Z	312.7	1440	215 Foundry Ave	Toronto	M5A 1B8	Canada
MTR009	TOU_DAY	34.9	false	AZ	CUST_GH888	2024-06-10T11:15:00Z	2.05	15	119 Pine St	Phoenix	85004	USA
MTR010	TOU_EVENING	51.7	true	CA	CUST_QW777	2024-06-10T17:45:00Z	4.34	30	821 Willow Ln	San Francisco	94109	USA
MTR011	BASIC	99.9	false	ENG	CUST_ER222	2024-06-10T03:00:00Z	0.12	5	5 Oakfield Ct	London	W1D 3QF	UK
MTR012	TOU_EVENING	64.8	true	BC	CUST_TT333	2024-06-10T20:00:00Z	5.51	60	102 Hillcrest Blvd	Vancouver	V5K 0A1	Canada
MTR013	blank	0	false	blank	CUST_YY999	2024-06-10T04:00:00Z	0.22	5	blank	blank	blank	blank
MTR014	TOU_EVENING	48.2	true	CA	CUST_KK555	2024-06-10T21:30:00Z	4.91	30	29 Cedar St	Los Angeles	90012	USA
MTR015	BUS_PEAK	18.3	true	PA	BUSS_MKT2	2024-06-10T13:00:00Z	86.5	120	1700 Market St	Philadelphia	19103	USA
MTR016	blank	0	false	blank	CUST_XF111	2024-06-10T00:00:00Z	0	1	blank	blank	blank	blank
MTR017	INDUSTRIAL	11.1	true	NSW	INDUSTRIAL_GIGA	2024-06-10T15:00:00Z	525.3	1440	77 Tech Park	Sydney	2000	Australia
MTR018	HOTEL_PREMIUM	8.4	true	MC	HOTEL_PRIME	2024-06-10T01:00:00Z	1320.9	1440	5 Beach Ave	Nice	06200	France
MTR019	TOU_DAY	41.6	false	OR	CUST_ZZ444	2024-06-10T06:30:00Z	2.76	15	221 Spruce St	Portland	97201	USA
MTR020	BASIC	59.2	false	CA	CUST_BB222	2024-06-10T17:00:00Z	3.98	60	350 Aspen Rd	San Jose	95112	USA

What the 200 rows show

from the 200-row sample

BASIC (tariff code) stands out: mean renewable_energy_percentage is 80.1, against 37.9 for the rest.

42%peak_usage_flag = true
32.9median renewable_energy_percentage
9meter location countries
46meter location cities
2.6median consumption_kwh
15median interval_minutes

Mean renewable_energy_percentage by tariff_code162 rows

renewable_energy_percentage200 rows, in bands of 10

Median 32.9, from 0.0 to 100.0.

meter_location_state162 rows with a value · 38 left blank

CA22
CO14
TX13
ENG13
WA11
MA10
ON10
FL9
IL7
NSW7

13 columns by typefrom the column list below

string 8
integer 1
float 2
datetime 1
boolean 1

Columns

13 columns in four groups

blueprint · 13 columns
column	type	description	example
Text 8 columns
`meter_id`	string	Unique identifier for each smart meter device	MTR001
`customer_id`	string	Unique identifier for the customer or household associated with the smart meter	CUST_AB123
`meter_location_street`	string	Street address where the smart meter is installedoptional	124 Maple Ave
`meter_location_city`	string	City where the smart meter is installedoptional	Chicago
`meter_location_state`	string	State or province where the smart meter is installedoptional	IL
`meter_location_postal_code`	string	Postal code of the smart meter installation addressoptional	60614
`meter_location_country`	string	Country of the smart meter installation address9 countries · optional	USA
`tariff_code`	string	Code representing the energy tariff applied to the meter at the time of reading8 codes · optional	TOU_DAY
Numbers 3 columns
`consumption_kwh`	float	Amount of energy consumed in kilowatt-hours during the recorded interval0 or more	2.15
`interval_minutes`	integer	Length of the measurement interval in minutes1 to 1,440	15
`renewable_energy_percentage`	float	Percentage of energy supplied from renewable sources during the interval0 to 100 · optional	32.5
Dates and times 1 column
`timestamp`	datetime	Date and time when the consumption reading was recorded	2024-06-10T07:15:00Z
True or false 1 column
`peak_usage_flag`	boolean	Indicates if the reading occurred during a designated peak usage periodoptional	false

Use it for

An energy dashboard
The peak_usage_flag rate, renewable_energy_percentage by tariff_code and a breakdown of meter_location_state. Excel, Power BI or Tableau.
Why do the 30 BASIC rows have a mean renewable_energy_percentage of 80.1?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Meters200
MTR00132.5TOU_DAY
MTR00245.2TOU_EVEN…
MTR00310BUS_PEAK
A software demo
Believable meters with customer_id, timestamp and consumption_kwh to fill a screen in front of a buyer.

This dataset200 rows13 columns

Yours1,000 rows13 columnsmeter_location_street: UK only

blueprint · smart-meter-consumption-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 13 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

Record hourly consumption per meter
Group by customer type
Flag peak demand events
Exclude personally identifiable information
Include meter installation date

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: smart-meter-consumption-patterns

What should your data show?

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Sample rows

What the 200 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?