US state grid-disruption intelligence (DOE OE-417, monthly)
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.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 13 728
- Colonnes
- 29
- Cadence de la source
- Annuelle
- Dernière actualisation
- 1 oct. 2026
- Thème
- economy
| Colonne | 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. |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| month | state_code | state_name | country_code | event_count | events_multi_state | events_severe_weather | events_physical_attack_vandalism | events_cyber | events_equipment_failure | events_fuel_supply | events_system_ops | events_wildfire | events_other | customers_affected | customers_unknown_count | max_single_event_customers | demand_loss_mw | fatalities | injuries | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 25 | — | 4 | — | — | https://www.oe.netl.doe.gov/docs/OE417_2002.xls | 2026-10-01 | 47d8e42f8df0 |
| 2002-01-01 | US-AR | Arkansas | USA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 35 | — | 4 | — | — | https://www.oe.netl.doe.gov/docs/OE417_2002.xls | 2026-10-01 | ad755fb76afd |
| 2002-01-01 | US-AZ | Arizona | USA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 42 | — | 4 | — | — | https://www.oe.netl.doe.gov/docs/OE417_2002.xls | 2026-10-01 | e465782ecd40 |
| 2002-01-01 | US-CA | California | USA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 35 | — | 4 | — | — | https://www.oe.netl.doe.gov/docs/OE417_2002.xls | 2026-10-01 | e9409ba7c82f |
| 2002-01-01 | US-CO | Colorado | USA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 44 | — | 4 | — | — | https://www.oe.netl.doe.gov/docs/OE417_2002.xls | 2026-10-01 | b8e7efa79d03 |
| 2002-01-01 | US-CT | Connecticut | USA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 36 | — | 4 | — | — | https://www.oe.netl.doe.gov/docs/OE417_2002.xls | 2026-10-01 | 89273e812e55 |
| 2002-01-01 | US-DE | Delaware | USA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 38 | — | 4 | — | — | https://www.oe.netl.doe.gov/docs/OE417_2002.xls | 2026-10-01 | 518b4f0740cb |
| 2002-01-01 | US-FL | Florida | USA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 35 | — | 4 | — | — | https://www.oe.netl.doe.gov/docs/OE417_2002.xls | 2026-10-01 | 9b14525f9243 |
| 2002-01-01 | US-GA | Georgia | USA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | — | 22 | — | 4 | — | — | https://www.oe.netl.doe.gov/docs/OE417_2002.xls | 2026-10-01 | 6f4b1be4fb8e |
Profilé le 1 oct. 2026 à partir de l’instantané 20261001T180353Z-1ee724de82b5
Mesuré- Complétude
- 90,3 %
- Lignes
- 13 728
- Colonnes
- 29
- Colonnes incomplètes
- 5
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| monthvarchar | 0 % | 222 | — |
|
| state_codevarchar | 0 % | 54 | — |
|
| state_namevarchar | 0 % | 59 | — |
|
| country_codevarchar | 0 % | 1 | — |
|
| event_countbigint | 0 % | 43 | 0 → 99médiane 0 | 133 hors du 1er–99e centile |
| events_multi_statebigint | 0 % | 9 | 0 → 9médiane 0 | 61 hors du 1er–99e centile |
| events_severe_weatherbigint | 0 % | 14 | 0 → 36médiane 0 | 76 hors du 1er–99e centile |
| events_physical_attack_vandalismbigint | 0 % | 10 | 0 → 8médiane 0 | 72 hors du 1er–99e centile |
| events_cyberbigint | 0 % | 3 | 0 → 2médiane 0 | |
| events_equipment_failurebigint | 0 % | 4 | 0 → 3médiane 0 | |
| events_fuel_supplybigint | 0 % | 4 | 0 → 3médiane 0 | |
| events_system_opsbigint | 0 % | 9 | 0 → 7médiane 0 | 55 hors du 1er–99e centile |
| events_wildfirebigint | 0 % | 4 | 0 → 3médiane 0 | |
| events_otherbigint | 0 % | 4 | 0 → 3médiane 0 | |
| customers_affectedbigint | 0 % | 1 328 | 0 → 11 212 625médiane 0 | 138 hors du 1er–99e centile |
| customers_unknown_countbigint | 0 % | 11 | 0 → 27médiane 0 | 57 hors du 1er–99e centile |
| max_single_event_customersbigint | 0 % | 1 005 | 0 → 4 200 000médiane 0 | 138 hors du 1er–99e centile |
| demand_loss_mwdouble | 0 % | 734 | 0 → 133 939médiane 0 | 138 hors du 1er–99e centile |
| fatalitiesbigint | 0 % | 1 | 0 → 0médiane 0 | |
| injuriesbigint | 0 % | 1 | 0 → 0médiane 0 | |
| avg_event_duration_hoursdouble | 84,2 % | 77 | 0 → 578 616médiane 0 | 19 hors du 1er–99e centile |
| severity_scorebigint | 0 % | 70 | 0 → 100médiane 37 | 126 hors du 1er–99e centile |
| pressure_ratio_12mdouble | 38,7 % | 900 | 0 → 876 000médiane 0 | 85 hors du 1er–99e centile |
| state_event_rankdouble | 1,9 % | 29 | 1 → 36médiane 8 | 75 hors du 1er–99e centile |
| share_severe_weather_pctdouble | 78,3 % | 29 | 0 → 100médiane 50 | |
| dominant_causevarchar | 78,3 % | 9 | — |
|
| source_urlvarchar | 0 % | 23 | — |
|
| collected_atvarchar | 0 % | 1 | — |
|
| row_hashvarchar | 0 % | 15 217 | — |
|
Dernier changement
20261001T175852Z-607537daa6de → 20261001T180353Z-1ee724de82b5
- Lignes actuelles
- 13 728 (+0)
- Nouvelles lignes
- 286
- Lignes retirées
- 286
- Lignes inchangées
- 13 442
Les lignes sont comparées comme des enregistrements entiers sur les colonnes communes aux deux versions ; une ligne modifiée compte pour une retirée et une nouvelle.
Mêmes colonnes et mêmes types que la version précédente.
- pressure_ratio_12m : valeurs manquantes 38,7 % → 38,7 %
- Actuelle
20261001T180353Z-1ee724de82b5 · sha256 1ee724de82b5…
13 728 lignes · +0 lignes par rapport à la précédente
20261001T175852Z-607537daa6de · sha256 607537daa6de…
13 728 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/us_grid_disruption_intel/us_state_grid_disruption_monthly" | jq '{title, rows, columns_count, license}'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)Point d’accès API : https://datazimuts.com/v1/datasets/us_grid_disruption_intel/us_state_grid_disruption_monthly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20261001T180353Z-1ee724de82b5 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
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/fr/datasets/us_grid_disruption_intel/us_state_grid_disruption_monthly?snapshot=20261001T180353Z-1ee724de82b5
@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/fr/datasets/us_grid_disruption_intel/us_state_grid_disruption_monthly?snapshot=20261001T180353Z-1ee724de82b5}},
note = {Snapshot 20261001T180353Z-1ee724de82b5, sha256 1ee724de82b59211a414a41a31ce7650ee9de48e11a8b355c046c95a18c3e03e; accessed 2026-10-02}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=us_grid_disruption_intel%2Fus_state_grid_disruption_monthly&lang=fr&theme=auto&snapshot=20261001T180353Z-1ee724de82b5&x=month&y=event_count&agg=avg" title="US state grid-disruption intelligence (DOE OE-417, monthly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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