Outage Prediction
“Which transformers will fail this month?”
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A real-world example
Which transformers will fail this month?
Unplanned outages cost US utilities $28B annually in repair costs, regulatory penalties, and customer compensation. Transformer failures alone account for 30% of outage minutes. Age-based replacement schedules waste $500K-$2M per premature replacement while missing failures in younger assets under stress. For a utility with 50,000 distribution transformers, predicting failures 30 days ahead saves $20-35M annually in emergency repair costs and prevents 40% of customer-minutes interrupted.
How KumoRFM solves this
Graph-powered intelligence for energy and utilities
Kumo connects assets, inspections, weather exposure, outage history, and load data into a grid reliability graph. The GNN learns failure patterns from the asset network: how weather stress accumulates on equipment clusters, how one transformer's failure increases load on neighbors, and how inspection findings predict cascading failures. PQL predicts monthly failure probability per transformer, prioritizing inspection and replacement spend.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
ASSETS
| asset_id | type | age_years | manufacturer | zone_id |
|---|---|---|---|---|
| TRX001 | Pole Transformer | 18 | ABB | ZONE-A |
| TRX002 | Pad Transformer | 8 | Siemens | ZONE-B |
| TRX003 | Pole Transformer | 25 | GE | ZONE-A |
INSPECTIONS
| inspection_id | asset_id | findings | score | date |
|---|---|---|---|---|
| INS501 | TRX001 | Oil discoloration | 72 | 2025-01-15 |
| INS502 | TRX002 | No issues | 95 | 2025-02-10 |
| INS503 | TRX003 | Bushing crack, oil leak | 38 | 2024-11-20 |
WEATHER
| zone_id | month | heat_days_above_95f | storm_events | salt_exposure |
|---|---|---|---|---|
| ZONE-A | 2025-02 | 8 | 2 | Low |
| ZONE-B | 2025-02 | 5 | 1 | High |
OUTAGE_HISTORY
| outage_id | asset_id | cause | duration_hours | date |
|---|---|---|---|---|
| OUT601 | TRX003 | Overload | 4.5 | 2024-08-15 |
| OUT602 | TRX001 | Lightning | 2.0 | 2024-07-22 |
LOAD_DATA
| asset_id | avg_load_pct | peak_load_pct | overload_events_30d |
|---|---|---|---|
| TRX001 | 72% | 94% | 3 |
| TRX002 | 55% | 68% | 0 |
| TRX003 | 88% | 105% | 8 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(OUTAGE_HISTORY.outage_id, 0, 30, days) FOR EACH ASSETS.asset_id
Prediction output
Every entity gets a score, updated continuously
| ASSET_ID | TYPE | AGE | FAILURE_PROB_30D | PRIORITY |
|---|---|---|---|---|
| TRX003 | Pole Transformer | 25 yrs | 0.82 | Critical |
| TRX001 | Pole Transformer | 18 yrs | 0.35 | Medium |
| TRX002 | Pad Transformer | 8 yrs | 0.04 | Low |
Understand why
Every prediction includes feature attributions — no black boxes
Asset TRX003 -- 25-year-old Pole Transformer in ZONE-A
Predicted: 82% failure probability in next 30 days (Critical)
Top contributing features
Overload events in last 30 days
8 events
30% attribution
Inspection score (bushing crack, oil leak)
38/100
26% attribution
Peak load exceeding rated capacity
105%
19% attribution
Previous outage history
1 overload failure
14% attribution
Neighboring transformer load increase
+15%
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A utility with 50,000 distribution transformers saves $20-35M annually by predicting failures 30 days ahead. Kumo's grid reliability graph detects compound stress patterns (overload + inspection findings + weather exposure) that age-based replacement schedules miss.
Related use cases
Explore more energy & utilities use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
