From Storm Response to Storm Readiness How Data and Analytics Are Reshaping Ontario’s Resilience Playbook
By Bob Champagne, Head of Digital Innovation and AI Enablement; Goutam Ghatak, VP of Grid Modernization; and Frank Carnevale, Country Head, Canada, iGreenTree.ai
When a severe ice storm sweeps through central Ontario, hundreds of thousands of customers can lose power, echoing the scale of historical events like the 1998 ice storm. Ontario’s distribution utilities mobilize mutual aid, replace broken poles, and restring lines, a familiar scene that reflects how they have handled major weather events for decades. But every storm leaves behind the same uneasy questions. How much of that damage was predictable? How many crews could have been better positioned before the first pole fell? How many customers received restoration estimates that turned out to be wrong, eroding trust at exactly the moment it mattered most? Regulatory expectations are making these questions harder to defer. Across Canada and the U.S., regulators are placing increasing emphasis on resilience, reliability, and transparent communication around restoration times, while extreme weather is striking more frequently and with greater intensity.
Ontario LDCs have spent years refining reactive storm response: faster dispatch, more coordinated mutual aid, sharper customer communication. Those investments have paid off. But the next leap comes from moving upstream, using data and analytics to anticipate where failures will happen, pre position crews, and deliver restoration estimates that customers can reliably act on. That shift does not require exotic technology. It requires integration. Weather
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Across Canada and the U.S., regulators are placing increasing emphasis on resilience, reliability, and transparent communication around restoration times, while extreme weather is striking more frequently and with greater intensity.
forecasts must connect with historical outage patterns. Asset condition data should align with vegetation risk and network topology. Automated Meter Infrastructure (AMI) last gasp signals, Outage Management System (OMS) tickets, and Geospatial Information System (GIS) geometry can work together to pinpoint trouble before a customer calls. Supervisory Control and Data Acquisition (SCADA) telemetry can feed models that learn from every past storm. The pieces exist in many utilities already; they simply need to speak the same language, flowing through a unified data platform that sits safely between OT systems and enterprise analytics. THE DISTRIBUTOR
SUMMER 2026
PHOTO: © BIBI / ADOBE STOCK
THE SHIFT FROM REACTIVE TO PREDICTIVE