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Learn how you can analyze and reduce your asset risk.

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Risk reduction analysis combining millions of fire behavior simulations with proprietary asset data.

Quantify risk from each asset and calculate potential risk reduction for asset hardening.

Energy utilities across the United States increasingly rely on wildfire spread prediction models to determine the risk associated with their assets. These wildfire simulations provide consequence measures that estimate acres burned, buildings destroyed, and population impacted. However, these consequence measures are conditional on the occurrence of an ignition and do not represent the expected risk of an asset. Energy utilities must not only understand the consequences of a wildfire resulting from their assets, but also the probability of that wildfire occurring from one asset over another to efficiently prioritize their grid-hardening and mitigation efforts.

Typically, asset hardening decisions are prioritized through a risk/spend efficiency (RSE) analysis, in which the asset risk reduction is divided by the cost of the mitigation. Asset hardening budgets are finite, so it is critical that wildfire mitigation plan properly reflect expected risk from assets to efficiently reduce wildfire impacts on utilities, local communities, and the surrounding nature

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Failure & Ignition Probability

Our model integrates equipment failure and ignition probability data for assets with individual spread predictions to determine which assets are most likely to fail, have an outage, and cause an ignition.

Optimizing Mitigation Effectiveness

Tools are provided to help utilities to design projects which optimize mitigation effectiveness by focusing on those assets with the highest expected risk.

Detailed Reporting

Detailed risk reduction reports are generated for each planned hardening project quantifying risk
at the individual asset, asset type, and circuit level.

Data-Driven Decisions

Provides utilities a framework to make informed, data-driven decisions about asset hardening to mitigate their wildfire risks.

Technosylva customers include leaders such as:

Advancing Your Utility’s Outage Analytics

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Outage or failure that results in a spark or burning material on the ground. Statistical models predict hourly outage probabilities using wind and asset attributes across all circuits.


Probability that burning material will create a wildfire that needs suppression. National Fire Danger Rating System Ignition Component uses fuel, fuel dryness, and wind to estimate probability of a fire starting from an ignition source

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Better understand your specific wildfire risk exposure and
next steps to mitigate your risk with one of our solutions specialists.