Monthly grid-disruption panel for the 50 US states, DC and Puerto Rico plus a US national aggregate (2002-01 .. 2023-12), built from the Department of Energy's Form OE-417 major electric disturbance event reports (3,874 events). Each (month, state) row carries event counts by cause category (severe weather, physical attack/vandalism, cyber, equipment failure, fuel supply, system operations, wildfire, other), customers affected, demand loss in MW, fatalities/injuries, and average event duration in hours, plus derived features: a 0-100 severity score (each geography scored against its own history), a 12-month pressure ratio of customers affected, a cross-state event heat rank, and the dominant cause. Units are counts, customers, and MW per calendar month. Caveat: OE-417 covers only major disturbances meeting DOE reporting thresholds (e.g. 50,000+ customers for 1+ hour), so small outages are invisible; reporting completeness improved after 2011; annual summaries lag, so 2023 is the latest year. A coincident disruption signal for demand, churn, and lead-scoring models — not a forecast.

Source: US Grid Disruption Intelligence (DOE OE-417)
Rows: 13,728
Columns: 29
Source cadence: Yearly
Last refreshed: Oct 1, 2026
Theme: economy

Column	Type	Description
month	string	ISO date of the first day of the calendar month.
state_code	string	ISO-3166-2 style geography code: US-XX for states/DC/PR; 'US' is the national aggregate row.
state_name	string	State/territory name; 'United States' for the national row.
country_code	string	ISO alpha-3 country code (USA).
event_count	integer	Number of DOE OE-417 major disturbance events in the month.
events_multi_state	integer	Events counted in this state that also hit other states (customers/demand split evenly across the hit states).
events_severe_weather	integer	Events classified as severe-weather caused.
events_physical_attack_vandalism	integer	Events classified as physical attack, vandalism, sabotage, or suspicious activity.
events_cyber	integer	Events classified as cyber events.
events_equipment_failure	integer	Events classified as equipment failure.
events_fuel_supply	integer	Events classified as fuel-supply emergencies.
events_system_ops	integer	Events classified as system-operations actions (load shedding, voltage reduction, public appeals, transmission interruptions, islanding).
events_wildfire	integer	Events classified as wildfire caused.
events_other	integer	Events not matching any cause rule (incl. earthquakes and DOE 'Other'-categorized events).
customers_affected	integer	Sum of reported customers affected (multi-state events split evenly; unparseable reports treated as 0 and counted in customers_unknown_count).
customers_unknown_count	integer	Events in the month with no parseable customer figure.
max_single_event_customers	integer	Largest single-event customer count in the month (post-split).
demand_loss_mw	float	Sum of reported demand loss in megawatts (post-split).
fatalities	integer	Sum of reported fatalities.
injuries	integer	Sum of reported injuries.
avg_event_duration_hours	float	Mean restoration-minus-begin hours over events reporting both dates; null when none.
severity_score	integer	0-100 within-geography disruption gauge: 50 + 12 * z(log1p(customers_affected)) over the geography's own 264-month history, clipped; 50 when history is flat.
pressure_ratio_12m	float	customers_affected / mean(customers_affected over the prior 12 months); null when fewer than 6 prior months exist or the baseline is 0.
state_event_rank	float	Rank of event_count across states within the month (1 = most events); null on the national row.
share_severe_weather_pct	float	100 * events_severe_weather / event_count; null when no events. (unit: percent)
dominant_cause	string	Modal cause category in the month; null when no events.
source_url	string	Official DOE annual XLS the month's source events came from.
collected_at	string	Date the source material was collected (ISO).
row_hash	string	md5 over month\|state_code\|event_count\|customers_affected\|severity_score.

First 10 sample rows — a preview, not the complete dataset.

month	state_code	state_name	country_code	avg_event_duration_hours	severity_score	pressure_ratio_12m	state_event_rank	share_severe_weather_pct	dominant_cause	source_url	collected_at	row_hash
2002-01-01	US-AL	Alabama	USA	—	25	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	47d8e42f8df0
2002-01-01	US-AR	Arkansas	USA	—	35	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	ad755fb76afd
2002-01-01	US-AZ	Arizona	USA	—	42	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	e465782ecd40
2002-01-01	US-CA	California	USA	—	35	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	e9409ba7c82f
2002-01-01	US-CO	Colorado	USA	—	44	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	b8e7efa79d03
2002-01-01	US-CT	Connecticut	USA	—	26	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	d602f4a84947
2002-01-01	US-DC	District of Columbia	USA	—	36	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	89273e812e55
2002-01-01	US-DE	Delaware	USA	—	38	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	518b4f0740cb
2002-01-01	US-FL	Florida	USA	—	35	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	9b14525f9243
2002-01-01	US-GA	Georgia	USA	—	22	—	4	—	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls	2026-10-01	6f4b1be4fb8e

Profiled Oct 1, 2026 from snapshot 20261001T180353Z-1ee724de82b5

Measured

Completeness: 90.3%
Rows: 13,728
Columns: 29
Columns with gaps: 5

Rows per yearmonth · 2002–2023 · 22 years

20022023

Column	Missing	Distinct	Range	Distribution
monthvarchar	0%	222	—	2002-01-0152 2002-02-0152 2002-03-0152
state_codevarchar	0%	54	—	US264 US-AL264 US-AR264
state_namevarchar	0%	59	—	Alabama264 Arizona264 Arkansas264
country_codevarchar	0%	1	—	USA13,728
event_countbigint	0%	43	0 → 99median 0	133 outside 1st–99th percentile
events_multi_statebigint	0%	9	0 → 9median 0	61 outside 1st–99th percentile
events_severe_weatherbigint	0%	14	0 → 36median 0	76 outside 1st–99th percentile
events_physical_attack_vandalismbigint	0%	10	0 → 8median 0	72 outside 1st–99th percentile
events_cyberbigint	0%	3	0 → 2median 0
events_equipment_failurebigint	0%	4	0 → 3median 0
events_fuel_supplybigint	0%	4	0 → 3median 0
events_system_opsbigint	0%	9	0 → 7median 0	55 outside 1st–99th percentile
events_wildfirebigint	0%	4	0 → 3median 0
events_otherbigint	0%	4	0 → 3median 0
customers_affectedbigint	0%	1,328	0 → 11,212,625median 0	138 outside 1st–99th percentile
customers_unknown_countbigint	0%	11	0 → 27median 0	57 outside 1st–99th percentile
max_single_event_customersbigint	0%	1,005	0 → 4,200,000median 0	138 outside 1st–99th percentile
demand_loss_mwdouble	0%	734	0 → 133,939median 0	138 outside 1st–99th percentile
fatalitiesbigint	0%	1	0 → 0median 0
injuriesbigint	0%	1	0 → 0median 0
avg_event_duration_hoursdouble	84.2%	77	0 → 578,616median 0	19 outside 1st–99th percentile
severity_scorebigint	0%	70	0 → 100median 37	126 outside 1st–99th percentile
pressure_ratio_12mdouble	38.7%	900	0 → 876,000median 0	85 outside 1st–99th percentile
state_event_rankdouble	1.9%	29	1 → 36median 8	75 outside 1st–99th percentile
share_severe_weather_pctdouble	78.3%	29	0 → 100median 50
dominant_causevarchar	78.3%	9	—	severe_weather1,505 system_ops678 physical_attack_vandalism646
source_urlvarchar	0%	23	—	https://www.oe.netl.doe.gov/docs/OE417_2002.xls624 https://www.oe.netl.doe.gov/docs/OE417_2003.xls624 https://www.oe.netl.doe.gov/docs/OE417_2004.xls624
collected_atvarchar	0%	1	—	2026-10-0113,728
row_hashvarchar	0%	15,217	—	0002833fea551 00055406af071 0007497858551

Latest change

20261001T175852Z-607537daa6de → 20261001T180353Z-1ee724de82b5

Rows now: 13,728 (+0)
New rows: 286
Removed rows: 286
Unchanged rows: 13,442

Rows are compared as whole records over the columns both versions share; an edited row counts as one removed and one new.

Same columns and types as the previous version.

pressure_ratio_12m: missing values 38.7% → 38.7%

Oct 1, 2026Current
20261001T180353Z-1ee724de82b5 · sha256 1ee724de82b5…
13,728 rows · +0 rows vs previous
Oct 1, 2026
20261001T175852Z-607537daa6de · sha256 607537daa6de…
13,728 rows · first snapshot

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/us_grid_disruption_intel/us_state_grid_disruption_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/us_grid_disruption_intel/us_state_grid_disruption_monthly").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/us_grid_disruption_intel/us_state_grid_disruption_monthly

Tip: fetch /llms.txt for the full machine-readable catalog.

Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.

Cite this snapshot

Pinned to snapshot 20261001T180353Z-1ee724de82b5 and its content hash, so readers get exactly the data you used.

APA

US Grid Disruption Intelligence (DOE OE-417). (2026). US state grid-disruption intelligence (DOE OE-417, monthly) [Data set, snapshot 20261001T180353Z-1ee724de82b5, sha256 1ee724de82b5]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/us_grid_disruption_intel/us_state_grid_disruption_monthly?snapshot=20261001T180353Z-1ee724de82b5

BibTeX

@misc{dz_us_grid_disruption_intel_us_state_grid_d_1ee724de,
  title        = {{US state grid-disruption intelligence (DOE OE-417, monthly)}},
  author       = {{US Grid Disruption Intelligence (DOE OE-417)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/us_grid_disruption_intel/us_state_grid_disruption_monthly?snapshot=20261001T180353Z-1ee724de82b5}},
  note         = {Snapshot 20261001T180353Z-1ee724de82b5, sha256 1ee724de82b59211a414a41a31ce7650ee9de48e11a8b355c046c95a18c3e03e; accessed 2026-10-02}
}

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US state grid-disruption intelligence (DOE OE-417, monthly)

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