Energy
17 columns
125 rows
8 formats

IoT Energy Optimization Metrics

This dataset provides granular, device-level energy consumption metrics from IoT sensors deployed in social media data centers. It includes power usage, environmental conditions, operational status, anomaly detection, and estimated costs and carbon emissions, enabling detailed analysis for sustainability, efficiency, and cost optimization. Ideal for operational managers and sustainability teams seeking actionable insights to reduce energy waste and environmental impact.

last updated 14 Jan 2026
by GoMask

The brief that made it

Identifying and addressing energy inefficiencies in data centers

Sample rows

preview · 8 of 125 rows · all 17 columns

record_idstring	device_typestring	power_usage_wattsfloat	anomaly_detectedboolean	data_center_idstring	timestampdatetime	data_center_namestring	device_idstring	device_locationstring	sensor_idstring	energy_consumption_kwhfloat	temperature_celsiusfloat	humidity_percentfloat	operational_statusstring	anomaly_typestring	energy_cost_usdfloat	carbon_emission_kgfloat
REC-0001-B1	server	376.2	false	DC-NY-01	2024-06-03T09:05:12Z	New York East	NY-SRV-001	Rack-01A	SEN-NY-001	11.4	24.7	53.2	active	none	1.08	0.68
REC-0002-B1	server	359.4	false	DC-NY-01	2024-06-03T09:15:27Z	New York East	NY-SRV-002	Rack-01B	SEN-NY-002	10.8	25.1	52.8	active	none	1.03	0.65
REC-0003-B1	cooling_unit	940.1	false	DC-NY-01	2024-06-03T09:25:41Z	New York East	NY-CU-001	Cooling-Block-A	SEN-NY-003	28.3	21.2	58.4	active	none	2.69	1.71
REC-0004-B1	network_switch	104.7	false	DC-NY-01	2024-06-03T09:32:18Z	New York East	NY-NWS-001	Rack-02C	SEN-NY-004	2.9	26.9	57.6	active	none	0.28	0.18
REC-0005-B1	storage	210.3	false	DC-NY-01	2024-06-03T09:36:45Z	New York East	NY-STO-001	Rack-03F	SEN-NY-005	6.3	27.2	54.9	active	none	0.6	0.38
REC-0006-B1	ups	125.6	false	DC-NY-01	2024-06-03T09:40:10Z	New York East	NY-UPS-001	Rack-UPS-1	SEN-NY-006	3.7	24.4	55.1	idle	none	0.35	0.22
REC-0007-B1	server	390.9	false	DC-NY-01	2024-06-03T09:45:18Z	New York East	NY-SRV-003	Rack-01C	SEN-NY-007	12.1	25.8	51.7	active	none	1.15	0.72
REC-0008-B1	cooling_unit	982.5	true	DC-NY-01	2024-06-03T09:52:24Z	New York East	NY-CU-002	Cooling-Block-B	SEN-NY-008	29.7	20.9	59.3	active	spike	2.83	1.8
REC-0009-B1	network_switch	98.1	false	DC-NY-01	2024-06-03T09:55:31Z	New York East	NY-NWS-002	Rack-02D	SEN-NY-009	2.7	27.1	56.4	active	none	0.26	0.16
REC-0010-B1	storage	202.8	false	DC-NY-01	2024-06-03T09:59:49Z	New York East	NY-STO-002	Rack-03G	SEN-NY-010	6	27.4	55.7	active	none	0.57	0.36
REC-0011-B1	server	384.5	false	DC-CA-01	2024-06-03T10:05:31Z	San Jose Central	CA-SRV-001	Rack-10A	SEN-CA-001	11.9	23.8	47.6	active	none	1.06	0.67
REC-0012-B1	cooling_unit	902.4	false	DC-CA-01	2024-06-03T10:12:08Z	San Jose Central	CA-CU-001	Cooling-Block-C	SEN-CA-002	27.1	18.9	51.9	active	none	2.43	1.55
REC-0013-B1	network_switch	121.9	false	DC-CA-01	2024-06-03T10:16:30Z	San Jose Central	CA-NWS-001	Rack-11B	SEN-CA-003	3.2	26.1	50.2	active	none	0.29	0.19
REC-0014-B1	storage	191.5	false	DC-CA-01	2024-06-03T10:19:22Z	San Jose Central	CA-STO-001	Rack-12E	SEN-CA-004	5.7	26.4	49.7	active	none	0.51	0.32
REC-0015-B1	ups	124.2	false	DC-CA-01	2024-06-03T10:25:33Z	San Jose Central	CA-UPS-001	Rack-UPS-2	SEN-CA-005	3.6	22.6	48.8	idle	none	0.32	0.2
REC-0016-B1	server	401.1	true	DC-CA-01	2024-06-03T10:29:44Z	San Jose Central	CA-SRV-002	Rack-10B	SEN-CA-006	12.5	24.2	47.3	active	overheating	1.14	0.72
REC-0017-B1	network_switch	115.3	false	DC-CA-01	2024-06-03T10:33:57Z	San Jose Central	CA-NWS-002	Rack-11C	SEN-CA-007	3	26.7	49.9	active	none	0.28	0.18
REC-0018-B1	storage	197.2	false	DC-CA-01	2024-06-03T10:38:11Z	San Jose Central	CA-STO-002	Rack-12F	SEN-CA-008	5.9	26.9	blank	active	none	0.54	0.34
REC-0019-B1	ups	119.1	false	DC-CA-01	2024-06-03T10:41:27Z	San Jose Central	CA-UPS-002	Rack-UPS-3	SEN-CA-009	3.4	21.8	47.5	idle	none	0.3	0.19
REC-0020-B1	server	378.3	false	DC-CA-01	2024-06-03T10:44:39Z	San Jose Central	CA-SRV-003	Rack-10C	SEN-CA-010	11.7	24.4	46.9	active	none	1.05	0.66

What the 125 rows show

from the 125-row sample

Cooling_unit (device type) stands out: mean power_usage_watts is 772.1, against 217.2 for the rest.

14%anomaly_detected = true
217.8median power_usage_watts
4operational statuses
6anomaly types
11data center names
3.0median energy_consumption_kwh

Mean power_usage_watts by device_type125 rows

power_usage_watts125 rows, in bands of 200

Median 217.8, from 41.1 to 1,101.

data_center_id125 rows · top 10 of 11 values

DC-WEU-0217
DC-SEU-0117
DC-NEU-0316
DC-NY-0110
DC-CA-0110
DC-TX-0110
DC-IL-0110
DC-FL-0110
DC-WEST-039
DC-EAST-028

17 columns by typefrom the column list below

string 9
float 6
datetime 1
boolean 1

Columns

17 columns in four groups

blueprint · 17 columns
column	type	description	example
Text 9 columns
`record_id`	string	Unique identifier for each energy consumption recordunique	REC-0001-B1
`data_center_id`	string	Unique identifier for the data center11 centers	DC-NY-01
`data_center_name`	string	Human-readable name of the data center11 names · optional	New York East
`device_id`	string	Unique identifier for the monitored device	NY-SRV-001
`device_type`	string	Type of device (e.g., server, cooling_unit, network_switch)6 values	server
`device_location`	string	Physical location or rack identifier within the data centeroptional	Rack-01A
`sensor_id`	string	Unique identifier for the IoT sensor capturing the data	SEN-NY-001
`operational_status`	string	Operational status of the device at the time of readingactive · idle · offline · maintenance · error · optional	active
`anomaly_type`	string	Type of anomaly detected (if any)6 values · optional	none
Numbers 6 columns
`energy_consumption_kwh`	float	Measured energy consumption in kilowatt-hours (kWh) for the device during the interval0 or more	11.4
`power_usage_watts`	float	Instantaneous power usage in watts at the time of reading0 or more · optional	376.2
`temperature_celsius`	float	Ambient temperature near the device in degrees Celsiusoptional	24.7
`humidity_percent`	float	Relative humidity near the device as a percentage (0-100)0 to 100 · optional	53.2
`energy_cost_usd`	float	Estimated cost of energy consumed during the interval, in USD0 or more · optional	1.08
`carbon_emission_kg`	float	Estimated carbon emissions (kg CO2 equivalent) for the energy consumed0 or more · optional	0.68
Dates and times 1 column
`timestamp`	datetime	Date and time when the energy reading was captured	2024-06-03T09:05:12Z
True or false 1 column
`anomaly_detected`	boolean	Indicates if an anomaly or inefficiency was detected by the monitoring systemoptional	false

Use it for

An energy dashboard
The anomaly_detected rate, power_usage_watts by device_type and a breakdown of data_center_id. Excel, Power BI or Tableau.
Why do the 23 cooling_unit rows have a mean power_usage_watts of 772.1?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Records125
REC-0001-B1376.2server
REC-0008-B1982.5cooling_…
REC-0016-B1401.1server
A software demo
Believable records with timestamp, data_center_id and data_center_name to fill a screen in front of a buyer.

This dataset125 rows17 columns

Yours1,000 rows17 columnsdevice_location: UK only

blueprint · iot-energy-optimization-metrics

Behind this dataset

Same schema. As many rows as you need.

These 125 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 a unique IoT device reading at a distinct timestamp.
Include real-time power usage (kWh), device ID, and server rack location.
Flag readings where energy exceeds device model baseline by 15%.
Include a calculated 'efficiency score' based on historical peer benchmarks.
Only active devices with at least 90% uptime in the past week are included.

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: iot-energy-optimization-metrics

What should your data show?

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Sample rows

What the 125 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?