Telecommunications
17 columns
182 rows
8 formats

Telecom Tower Smart Meter Dataset

This dataset contains granular IoT smart meter readings from telecom towers, including energy consumption, voltage, current, and anomaly detection flags, along with detailed location data. It is designed for advanced analytics, predictive maintenance, and ESG reporting, enabling telecom and PropTech professionals to optimize tower operations and sustainability initiatives.

last updated 4 Dec 2025
by GoMask

The brief that made it

Energy consumption trend analysis for telecom towers

Sample rows

preview · 8 of 182 rows · all 17 columns

reading_idstring	anomaly_typestring	energy_consumed_kwhfloat	anomaly_detectedboolean	location_citystring	tower_idstring	meter_idstring	timestampdatetime	voltage_vfloat	current_afloat	power_factorfloat	location_street_addressstring	location_statestring	location_postal_codestring	location_countrystring	maintenance_flagboolean	esg_reporting_flagboolean
RDG-00001	none	48.7	false	Boston	TWR-001	MTR-001-A	2024-06-01T08:15:00Z	230.1	21.3	0.91	123 Beacon St	MA	02116	USA	false	true
RDG-00002	none	56.2	false	San Jose	TWR-002	MTR-002-B	2024-06-01T09:00:00Z	120.4	26.7	0.83	200 Fremont Ave	CA	95112	USA	false	true
RDG-00003	outage	0	true	Paris	TWR-003	MTR-003-C	2024-06-01T09:30:00Z	0	0	0	55 Rue de Rivoli	IDF	75001	France	true	false
RDG-00004	none	215.3	false	Manchester	TWR-004	MTR-004-D	2024-06-01T10:05:00Z	380.2	45.6	0.74	78 Oxford Rd	GTM	M13 9PL	UK	false	true
RDG-00005	spike	375.2	true	Bangalore	TWR-005	MTR-005-E	2024-06-01T10:45:00Z	415	53.2	0.62	12 MG Road	KA	560001	India	true	false
RDG-00006	none	36.8	false	Sydney	TWR-006	MTR-006-F	2024-06-01T11:15:00Z	229.9	19.7	0.96	17 Queen St	NSW	2000	Australia	false	true
RDG-00007	drop	0.03	true	Cape Town	TWR-007	MTR-007-G	2024-06-01T11:59:00Z	0.2	0.1	0.05	44 Alexander St	WC	8001	South Africa	true	false
RDG-00008	none	70.1	false	New York	TWR-008	MTR-008-H	2024-06-01T12:30:00Z	240	27.8	0.85	99 Broadway	NY	10010	USA	false	true
RDG-00009	spike	9999.9	true	San Jose	TWR-009	MTR-009-I	2024-06-01T13:00:00Z	480	98.7	0.05	500 Tech Park	CA	95134	USA	true	false
RDG-00010	drop	0.09	true	Denver	TWR-010	MTR-010-J	2024-06-01T13:30:00Z	blank	blank	blank	23 Market St	CO	80202	USA	true	false
RDG-00011	none	86.2	false	Washington	TWR-011	MTR-011-K	2024-06-01T14:00:00Z	120	24.9	0.87	1600 Pennsylvania Ave	DC	20006	USA	false	true
RDG-00012	outage	0	true	London	TWR-012	MTR-012-L	2024-06-01T14:45:00Z	0	0	0	1 King St	LDN	EC2V 8AU	UK	true	false
RDG-00013	none	66.7	false	Melbourne	TWR-013	MTR-013-M	2024-06-01T15:15:00Z	220.7	23.2	0.82	88 Queen's Rd	VIC	3004	Australia	false	true
RDG-00014	drop	0.5	true	Berlin	TWR-014	MTR-014-N	2024-06-01T16:00:00Z	0.1	0.2	0.13	120 Main St	BE	10115	Germany	true	false
RDG-00015	none	112.8	false	London	TWR-015	MTR-015-O	2024-06-01T16:30:00Z	320.6	27.6	0.73	75 St Pauls	LDN	EC1A 7AJ	UK	false	true
RDG-00016	spike	987.3	true	Bangalore	TWR-016	MTR-016-P	2024-06-01T17:05:00Z	415	92.1	0.09	101 MG Road	KA	560010	India	true	false
RDG-00017	none	34.2	false	Seattle	TWR-017	MTR-017-Q	2024-06-01T17:30:00Z	230	20.9	0.88	120 Pine St	WA	98101	USA	false	true
RDG-00018	drop	0.02	true	Cape Town	TWR-018	MTR-018-R	2024-06-01T18:00:00Z	0.2	0.1	blank	19 Green St	WC	8002	South Africa	true	false
RDG-00019	none	62.7	false	London	TWR-019	MTR-019-S	2024-06-01T18:45:00Z	220.2	22.3	0.82	14 Park Lane	LDN	W1K 7AF	UK	false	true
RDG-00020	none	24.5	false	Melbourne	TWR-020	MTR-020-A	2024-06-01T19:15:00Z	229.7	18.2	0.94	88 Collins St	VIC	3000	Australia	false	true

What the 182 rows show

from the 182-row sample

Spike (anomaly type) stands out: mean energy_consumed_kwh is 6,932, against 51.2 for the rest.

48%anomaly_detected = true
50.5median energy_consumed_kwh
9location countries
27location states
228.0median voltage_v
17.9median current_a

Mean energy_consumed_kwh by anomaly_type182 rows

energy_consumed_kwh182 rows, in bands of 1,000

Median 50.5, from 0.0 to 10,000.

location_city182 rows · top 10 of 27 values

San Jose18
Sydney15
London15
Bangalore14
Paris13
Toronto13
Manchester12
Cape Town12
Melbourne11
Boston10

17 columns by typefrom the column list below

string 9
float 4
datetime 1
boolean 3

Columns

17 columns in four groups

blueprint · 17 columns
column	type	description	example
Text 9 columns
`reading_id`	string	Unique identifier for each smart meter reading recordunique	RDG-00001
`tower_id`	string	Unique identifier for the telecom tower where the meter is installed	TWR-001
`meter_id`	string	Unique identifier for the smart meter device	MTR-001-A
`anomaly_type`	string	Type of anomaly detected (if any), e.g., 'spike', 'drop', 'outage'spike · drop · outage · none · optional	none
`location_street_address`	string	Street address of the telecom toweroptional	123 Beacon St
`location_city`	string	City where the telecom tower is locatedoptional	Boston
`location_state`	string	State or province of the telecom tower locationoptional	MA
`location_postal_code`	string	Postal code of the telecom tower locationoptional	02116
`location_country`	string	Country where the telecom tower is located9 countries · optional	USA
Numbers 4 columns
`energy_consumed_kwh`	float	Total energy consumed in kilowatt-hours during the reading interval0 or more	48.7
`voltage_v`	float	Measured voltage in volts at the time of reading0 or more · optional	230.1
`current_a`	float	Measured current in amperes at the time of reading0 or more · optional	21.3
`power_factor`	float	Power factor recorded by the meter (between 0 and 1)0 to 1 · optional	0.91
Dates and times 1 column
`timestamp`	datetime	Date and time when the meter reading was recorded	2024-06-01T08:15:00Z
True or false 3 columns
`anomaly_detected`	boolean	Indicates if an anomaly was detected during this readingoptional	false
`maintenance_flag`	boolean	Indicates if the tower is scheduled for maintenance based on readingsoptional	false
`esg_reporting_flag`	boolean	Indicates if this reading is included in ESG reporting workflowsoptional	true

Use it for

A telecommunications dashboard
The anomaly_detected rate, energy_consumed_kwh by anomaly_type and a breakdown of location_city. Excel, Power BI or Tableau.
Why do the 25 spike rows have a mean energy_consumed_kwh of 6,932?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Readings182
RDG-0000148.7none
RDG-000030outage
RDG-00005375.2spike
A software demo
Believable readings with tower_id, meter_id and timestamp to fill a screen in front of a buyer.

This dataset182 rows17 columns

Yours1,000 rows17 columnslocation_street_address: UK only

blueprint · telecom-tower-smart-meter-dataset

Behind this dataset

Same schema. As many rows as you need.

These 182 rows came out of a blueprint — 17 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 tower's hourly reading
Include meter ID, tower location, energy usage (kWh), and device status
Flag readings exceeding 10% of monthly baseline as anomalies
Exclude towers with less than 95% device uptime in the previous week
Include temperature and humidity data for environmental correlation
Missing readings are marked with 'NaN' and flagged for maintenance follow-up

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: telecom-tower-smart-meter-dataset

What should your data show?

No signup. No card.

Sample rows

What the 182 rows show

Columns

Use it for

A telecommunications dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Telecommunications

What should your data show?