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 environment.
Technosylva customers include leaders such as:
