Quantify your risk from each asset and calculate potential risk reduction for asset hardening.
Advancing Your Utility’s Outage Analytics
A detailed analysis of utility wildfire risk, using historical weather, outage analytics, and wildfire simulations
Our platform provides the information necessary to help prioritize asset hardening and vegetation management decisions. This provides the basis for quantifying risk reduction and risk/spend efficiencies. Risk reduction analysis allows you to quantify risk from each asset and calculate potential risk reduction for asset replacement or vegetation trimming and clearing.
- Design mitigation projects to optimize mitigation effectiveness for asset hardening and vegetation management by prioritizing those assets with the highest expected risk.
- Detailed risk reduction reports are also generated for each planned hardening project quantifying risk at the individual asset segment, asset type, and circuit level.
Probability of Outage & Failure
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 of Ignition
Probability that burning material will create a wildfire that requires suppression. The National Fire Danger Rating System Ignition Component uses fuel type, fuel dryness, and wind speed to estimate the probability of a fire starting from an ignition source
Advantage of Technosylva’s Wildfire Risk Mitigation Modeling
Energy utilities across the United States increasingly rely on Technosylva’s 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.
Conditional vs. Expected Risk
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.
