Outage Operations

About

Extreme weather is the leading cause of power outages in the United States, and the decisions utility operations teams make in the hours and days before a storm arrives directly impact restoration times, customer satisfaction, and operational costs. Pre-positioning crews, securing mutual aid, and communicating proactively with customers all depend on having an accurate, spatially specific picture of where outages are likely to occur and how many to expect (information that most utilities currently piece together from past experience and institutional knowledge rather than a quantitative forecast).

Technosylva’s Outage Operations addresses this gap by delivering a five-day forecast of expected weather-driven outage events at both the territory and service center level. Updated twice daily and covering all major weather-related outage drivers (including high wind, rain, snow, ice, and extreme temperatures) Outage Operations provides emergency planning teams with a data-driven foundation for pre-storm resource decisions in the hours and days before an event arrives.

The product combines each utility’s own historical outage records with up to 20 years of Technosylva's proprietary high-resolution weather model reanalysis data at 2 km resolution during training. This is the same meteorological dataset underlying Technosylva's wildfire products. This ensures Outage Operations predictions reflect the specific infrastructure, vegetation environment, and topography of that territory rather than a generic regional estimate. The achievable spatial and temporal resolution is assessed during onboarding.

For utilities that have extended their partnership with Technosylva beyond outage prediction, all hazard products (including wildfire and flood) are accessible within the same Operations interface, providing a unified situational awareness environment for multi-hazard emergency planning.

Key Use Cases

Pre-Event Tracking

• Storm Monitoring: Utility managers can track weather forecast conditions and quantitative outage event projections up to five days ahead, with forecasts updated twice daily as new meteorological data becomes available. This continuous update cycle ensures that planning decisions are always based on the latest available weather guidance rather than a static snapshot taken days before an event.

• Impact Insights: Outage Operations identifies where outages are most likely to occur across the service territory at service center and sub-service center granularity, allowing teams to focus attention and resources on the areas of greatest projected need rather than treating the entire territory uniformly.

• Outage Type Forecasting: For utilities with sufficient data quality, the Predict Plus tier identifies the expected types of outages. This breakdown allows operations teams to anticipate not just how many outages to expect, but what types of repairs will be needed, enabling more efficient pre-positioning of specialized crews and materials.

Resource Planning

• Crew Pre-Staging: Because Outage Operations provides spatially explicit outage forecasts several days in advance, operations managers can begin scheduling crews and equipment well before event arrival, matching resource type and location to the forecast rather than reacting after the fact.

  • Predict provides the total number of expected outages that may be sufficient for some utilities to plan for the number of resources.

  • Predict Plus can provide additional visibility into the types of crews that will be needed.

  • Restore tier takes this a step further by calculating the number of restoration resources required to meet a utility's desired restoration timeline given the forecasted outage count and damage mix.

• Material Procurement: For utilities with the Predict Plus tier, damage type forecasts support estimates of equipment quantities (poles, transformers, and other restoration materials) needed to meet the anticipated repair mix. Output granularity is dependent on the quality of the utility's historical outage records.

• Mutual Aid Coordination: Early quantitative forecasts give utilities enough lead time to communicate resource requirements to neighboring utilities and coordinate mutual aid agreements before an event arrives, rather than scrambling during active restoration.

Operational Decision Support

• Priority Setting: During an active event, Outage Operations' forecasts help operations teams focus restoration resources on the areas with the highest predicted impact, supporting more efficient crew call-outs, dispatch, and sequencing of repair crews. Depending on the size of the impact, a utility may decide to stand up an emergency operations center (EOC) to effectively manage restoration efforts.

• Cost Projections: Daily outage event count and restoration time estimates provide the inputs needed to make informed resource allocation decisions and support financial planning before and during a storm.

• Customer Communication: Outage counts and estimated restoration time forecasts support proactive outreach to customers and call center teams, reducing inbound call volume and improving customer experience during high-stress weather events.

Methodology

Outage Operations uses machine learning technology to predict weather-driven outages from meteorological inputs and utility outage history. The model is trained separately for each utility using the client's historical records and Technosylva's proprietary 20-year, 2 km Weather Research and Forecasting (WRF) weather reanalysis, ensuring that predictions reflect the unique infrastructure, vegetation environment, and topography of each service territory.

The 20-year reanalysis dataset is the same meteorological foundation used across Technosylva's industry leading wildfire products, providing a consistent statistical basis for all weather hazards modeled on the platform. Predictions cover a five-day forecast horizon and are updated twice daily using the 00Z and 12Z weather forecast cycles.

Live Model Inputs

• Weather Data: Technosylva’s proprietary WRF weather forecast model is used to provide all the surface and upper air weather variables determined to be relevant to predicting customer outages. The WRF model runs 4x per day at 00Z, 06Z, 12Z and 18Z with a 5-day lead time. 

• Outage Data: Utility-supplied historical outage records. Output granularity depends on the volume, quality, and spatial specificity of the utility's historical data.

Model Outputs

• Predict: Total predicted sustained outage events by service center and high-resolution hexagonal grid for a rolling five-day forecast period. Predictions are updated 4x daily using the weather forecast cycles. The granularity of spatial outputs is assessed during the utility data review conducted at onboarding and may vary by customer.

• Predict Plus: Breaks the total forecasted outage count into expected damage categories — such as vegetation-related failures, overhead distribution damage, equipment and device failures, and underground system impacts. This breakdown allows operations teams to anticipate not just how many outages to expect, but what types of repairs will be needed, enabling more efficient pre-positioning of specialized crews and materials.

• Restore: Calculates the number of restoration resources required to meet a utility's desired restoration timeline (24h, 48h, 72h, etc.) given the forecasted outage count and damage mix. 

Model Assumptions and Limitations

• Weather Forecast Dependency. Outage Operations is an input-output system whose outage predictions are grounded in the same meteorological guidance that drives operational decision-making across the utility industry. Forecast confidence naturally improves as an event approaches and meteorological uncertainty narrows. The twice daily update cycle ensures that predictions remain as current as the underlying weather science allows, giving operations teams the most relevant guidance at every stage of storm preparation.

• Historical Data Integrity. Outage Operations learns from each utility’s own historical outage records, meaning the quality and completeness of those records directly shapes model performance. The onboarding process includes a structured data review to identify and address common issues such as miscoded damage causes or reporting gaps. Utilities that maintain consistent outage records over time will see corresponding improvements in model accuracy as retraining incorporates richer historical data.

• Granularity of Cause Data for the Predict Plus Tier. Accurately predicting damage types is reliant on quality cause or action codes being recorded in the utility’s outage management system. Utilities with detailed, well-maintained cause coding practices are best positioned to benefit from this tier. If cause code data is more limited, the standard Predict tier will  deliver reliable outage event count forecasts. Technosylva would be happy to partner and assist with recommendations to improve customer damage data collection as a pathway to Predict Plus in the future. 

• Vegetation and Maintenance Cycle Visibility. Outage Operations is trained on historical outage patterns, which reflect average infrastructure and vegetation conditions across the training period. Periodic model retraining progressively updates the model’s representation of current infrastructure conditions, incorporating the most recent outage history over time.

• Extreme and Unprecedented Events. Like all statistical models, Outage Operations performs most reliably within the range of conditions well-represented in its training data. For rare, high-magnitude events, Outage Operations provides a quantitative starting point that operations teams can calibrate alongside expert meteorological judgment. In these scenarios, integrating Outage Operations’ forecast with situational awareness from your meteorological team delivers the most reliable operational guidance.

Interface Overview