Reducing SAIDI, SAIFI, and CAIDI with Smarter Weather Forecasting
In today’s climate-challenged environment, utility performance isn’t judged solely on uptime—it’s measured in minutes and megawatts. Metrics like SAIDI (System Average Interruption Duration Index), SAIFI (System Average Interruption Frequency Index), and CAIDI (Customer Average Interruption Duration Index) are core to evaluating how effectively utilities serve their customers and safeguard their infrastructure.
But here’s the challenge: the weather is becoming more volatile, more intense, and harder to predict using traditional tools. Outages linked to severe storms, flash floods, tropical systems, and extreme heat are now both more frequent and more impactful. Reducing their consequences demands faster, more accurate, and more localized weather intelligence.
That’s where next-generation forecasting comes in. Powered by Climavision’s Horizon AI suite of models and proprietary radar network, utilities now have access to the high-resolution, early-warning data they need to reduce outage frequency, restore power faster, and improve reliability across the board.
In Summary:
- Traditional weather models fall short for utility needs, offering limited lead time, poor spatial resolution, and gaps in low-level radar coverage that leave critical threats undetected.
- Climavision’s Horizon AI HIRES model delivers high-resolution forecasts up to 7 days in advance, outperforming public models in both accuracy and range—giving utilities more time to prepare for storms and reduce outages.
- Horizon AI Point provides hyper-local, asset-specific forecasts that help utilities fine-tune demand, optimize maintenance, and accelerate restoration efforts—directly improving SAIDI, SAIFI, and CAIDI.
- Radar as a Service (RaaS) fills national coverage gaps by providing low-level radar data essential for detecting wind, rain, and storm structures that traditional networks often miss.
- Case studies from ConEd, Texas Hill Country, and PJM illustrate how Climavision’s forecasting suite could have enabled faster, smarter decisions—preventing outages, mitigating damage, and minimizing financial risk.
Why Traditional Forecasting Falls Short for Utilities
While widely used public weather models like HRRR and GFS provide a starting point for forecasting, they weren’t built with utility operations in mind. Their constraints include:
- Short forecast ranges (often 18–48 hours for high-resolution models),
- Lower spatial resolution that misses localized threats,
- Limited refresh rates, and
- Lack of integration with local infrastructure or sensors.
Even more critically, many regions across the U.S.—particularly rural, mountainous, or coastal areas—are affected by radar gaps below 1km above ground level. These blind spots leave utilities unaware of near-surface hazards like straight-line winds, low-level rotation, flash flooding, and hail. In high-stakes storm situations, missing these details can translate into slower response, greater damage, and more customer minutes lost.
The Opportunity: How Advanced Forecasting Improves Reliability Metrics
Climavision’s integrated weather intelligence stack is designed specifically to fill these gaps and support utilities in reducing all three major reliability metrics.
Lower SAIFI – System Average Interruption Frequency
SAIFI reflects how often the average customer experiences an outage. With longer lead times and more spatially precise alerts, utilities can take preemptive action—like staging crews, trimming vegetation in high-risk zones, or delaying maintenance near impact windows.
- Horizon AI HIRES, for example, delivers high-resolution guidance up to 7 days in advance, far exceeding the 1–2 day range of publicly available high-res models. This extended lead time allows grid operators to shift from reactive to proactive.
Reduce SAIDI – System Average Interruption Duration
The total duration of outages depends on how quickly and effectively a utility can detect damage and mobilize crews. That process starts with accurate, localized forecasts.
- Horizon AI Point offers sub-hourly, asset-specific forecasting, enabling operations centers to monitor weather risk at the circuit, feeder, or substation level.
- With real-time radar coverage from RaaS, utilities can detect and verify hazards as they develop—especially in previously uncovered areas.
This translates into shorter response times and more efficient restoration, driving SAIDI downward.
Lower CAIDI – Customer Average Interruption Duration
When outages do occur, reducing the duration for each affected customer is a critical performance indicator. The key? Knowing where to send crews, how much support they’ll need, and what hazards they’re likely to encounter.
- Advanced wind, precipitation, and lightning forecasts from HIRES and Point allow dispatch teams to prioritize resources based on expected storm intensity and location.
- This minimizes over-response in lightly affected areas and ensures hard-hit communities get the support they need faster.
Real-World Impact: Three Forecast-Driven Case Studies
ConEd – Evening Thunderstorms, New York City (July 14, 2025)
A line of thunderstorms swept through the ConEd service area during peak evening hours, causing localized power outages driven by lightning and 30+ mph wind gusts. While the publicly available HRRR model suggested a later storm arrival and underestimated wind speeds, Climavision’s HIRES model forecasted the correct timing and intensity nearly two full days in advance.
This early insight can give utilities the opportunity to plan for after-hours response, adjust crew schedules, and communicate risk to customers well ahead of the event. Even moderate wind gusts in an urban environment can cause tree damage and trigger outages—especially during summer demand peaks.
Texas Hill Country – Catastrophic Flash Flooding (July 4, 2025)
An intense storm system dumped 10–12 inches of rain in just a few hours, triggering deadly flash flooding along the Guadalupe River. Most global models struggled to identify the threat until 1–2 days before the event—but Climavision’s Horizon AI Global model picked up on the rainfall potential six days out, and HIRES locked onto 12-inch rainfall totals more than 24 hours in advance, ahead of any other high-res model.
With better guidance, utilities could have pre-staged flood response equipment, alerted at-risk substations, and coordinated with emergency managers to better protect vulnerable infrastructure.
PJM Interconnection – Prolonged Heatwave and Load Surge (June 2024)
Over the course of a week, temperatures in PJM territories soared into the 90s and 100s, pushing demand far beyond normal levels and threatening brownouts and blackouts. Peak load exceeded 150,000 MW—well above average summer norms.
Horizon AI Point forecasts accurately predicted the multi-day stretch of extreme heat at the asset level, which could have enabled grid operators and market participants to anticipate demand spikes, optimize generation dispatch, and reduce price volatility. On June 22, prices at the PJM Eastern Hub surged to nearly $94/MWh—nearly triple the normal range. With the right insights, utilities could have better balanced load and avoided market strain.
The Full Forecasting Stack for Utility Operators
Horizon AI Global
- Early warning system for long-range threats
- Ideal for planning grid hardening, outage drills, and generation strategy
- 10–15 day outlook with storm and precipitation risk identification
Horizon AI HIRES
- 2 km CONUS domain with optional 0.67 km sub-domains
- Forecasts out to 7 days—unmatched among high-resolution models
- Accurate for wind gusts, hail, tornado potential, and localized precipitation
Horizon AI Point
- Tailored forecasts at the asset level
- Incorporates local sensors and AI bias correction
- Delivers hyper-local demand forecasting, outage planning, and O&M insights
Radar as a Service (RaaS)
- Proprietary low-level radar network that fills gaps left by national systems
- Essential for flash flood detection, wind shear monitoring, and near-surface storm tracking
- Directly improves nowcasting and event verification during outages
Reliability Begins with Visibility
Outages are unavoidable—but how a utility prepares for, responds to, and recovers from weather events defines the customer experience. To improve SAIDI, SAIFI, and CAIDI, utilities must integrate next-generation weather intelligence into every layer of their operations—from long-range planning to real-time decision-making.
With Climavision’s Horizon AI forecasting models and radar technology, utilities gain the lead time, precision, and asset-level insight they need to protect their customers and the grid—no matter the forecast.
Want to see how Climavision can support your grid resiliency goals and reliability KPIs? Schedule a demo today and explore the most advanced weather intelligence platform built for utility operations.
