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Case study

Predicting where a storm will break the grid — five days out

The utility could see a storm coming. What it couldn't see was which districts would fail, how badly, or where to put its crews before the first pole came down.

Client
Southern California Edison
Sector
Energy · Utilities
Role
Subcontracted delivery
Period
2 years
Result
15% reduction in outage times, client-reported
reduction in outage times, client-reported
15%reduction in outage times, client-reported
forecast horizon
5 daysforecast horizon
model stages
2model stages

The problem

Storm response at a large utility is a staging problem. Crews, transformers, poles and wire have to be in roughly the right place before the weather arrives, because moving them afterward is what turns a six-hour outage into a two-day one.

The utility had weather forecasts and it had asset records. What it didn't have was anything connecting the two: no way to turn "a storm is coming" into "these districts will take damage, of roughly this magnitude, needing roughly these resources."

What we built

A two-stage predictive model, deliberately split so each stage answers one question and can be validated on its own.

Stage one — will it break?
Predicts the likelihood of damage occurring in a given district.
Stage two — how badly?
Predicts damage to specific grid assets within districts flagged by stage one.
Resource model
Converts predicted damage into the crews, materials and hours required — the output the operations team acts on.

The two-stage storm damage model

Two-stage storm damage model over a service territoryA forecast cone crosses the utility's service territory, widening with lead time. Stage one scores every district for the likelihood of damage; the districts crossing the threshold are outlined. Stage two runs only inside those districts, scoring individual poles and transformers along the distribution feeders — and the predicted failures cluster under tree canopy, because in most storms something falls on the grid rather than the grid failing on its own. A resource model turns that into crews, materials and hours, up to five days before the storm arrives.SERVICE TERRITORYONE FLAGGED DISTRICTFORECAST TRACK · CONE WIDENS WITH LEAD TIMEEvery district scored. Most will be fine.FEEDER · POLES · CANOPY12 predicted failures, every one of them under canopy.STAGE 01 · WHICH DISTRICTSSTAGE 02 · WHICH ASSETSCREWS + MATERIALS STAGEDFORECAST ISSUEDSTORMUp to five days of lead time to stage crews and materials.
likelihood of damageflagged by stage 01predicted asset failureStage one is cheap and runs over the whole territory; stage two is expensive and runs only where stage one fired. That keeps asset-level prediction affordable to run daily, with the answer arriving while there's still time to act on it.

The data

Weather feeds from multiple open sources, automatically ingested and prepared. Static asset data — the number, age and location of poles, transformers and other equipment exposed to a given storm track. And the geospatial layer that turns out to matter most: tree cover and soil moisture, because trees and debris striking lines cause most storm damage.

How it was delivered

A cloud-hosted dashboard with interactive maps showing predicted damage by district, the resources required, and the hours to restore. Automated severe-weather alerts by email so the model reaches people who aren't sitting in front of a dashboard at 4am.

Result

The client reported outage times falling by 15%, with damage predicted up to five days ahead — enough lead time to stage crews and materials before the storm arrives.

Five days is roughly how long it takes to move crews, which is why the forecast horizon matters as much as the accuracy of the damage model.

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