Extreme Weather Events Showcase Value Of Machine Learning

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Across much of the country, the holidays were ushered in with strong winter storms, with every region of the country impacted by extreme weather from Christmas into the new year. The West Coast has experienced the most recent extreme weather with its third atmospheric river event in just three weeks bringing record rainfall and winds along the California coast. Buffalo, NY, is still digging out from its record holiday snowstorm and much of the Northern Plains saw record snowfalls just last week. The southern states weren’t spared from extreme winter weather with Florida and other parts of the southeast experiencing record cold temperatures over Christmas. While these weather events are all quite different — from heavy snows to rain to extreme cold — the one thing they have in common is the challenge it brings to the 1,400 utility companies across the country.

Extreme weather events are increasing, and utilities and customers alike are feeling the impact. According to Climate Central, the United States has experienced a 67% increase in major power outages from weather-related events since 2000, a trend predicted to continue as extreme weather events increase in frequency. With these most recent weather events across the country, on Christmas Eve alone, over 1.6 million U.S. customers were without power. The troubles continue now in California with nearly 196,000 Californians without power last week and more storms over the Pacific ready to move in bringing similar conditions.

Large utilities have been effectively using predictive weather analytics to prepare and pre-stage crews to help manage restoration efforts during major weather events like we just saw. But the majority of small to mid-size utilities still rely on traditional forecasts, severe weather updates and immediate impact assessments to make decisions around operations, safety and resource allocation. Recent advancements in technology, data modeling and cloud computing are making enterprise technology, such as machine learning for weather risks, more accessible to utilities of all sizes.

So, what’s the benefit of using machine learning to help identify and manage weather risks? When extreme weather events occur, utilities need an effective storm response to minimize impact to power, infrastructure, and travel. As recent weather events have shown, this is often easier said than done. With multiple factors, such as incident types, trouble locations, and how many crews are needed – not necessarily just the number of customers impacted – it makes it challenging to estimate resources.

Using machine learning along with established risk thresholds, such as ice accretion or wind gusts, a utility gets guidance on the escalation level needed and where to reallocate and source additional restoration crews and materials, as needed, ahead of the event. After the event, it can justify pre-staging costs that could be recovered.

According to Deloitte, utility customers’ behaviors and expectations are changing, such as more wanting more high-tech solutions, and greater flexibility in outage prevention and response. System hardening focuses on installing more resistant equipment and may include using stronger, more resilient power poles or burying lines underground. Hardening programs can take decades to see results, so having access to predictive weather technology offers more immediate results, can be localized to specific parts of the grid or area of operation, and used alongside longer-term, infrastructure improvements.

With the compounding pressures of increasing extreme weather events, increased outages and subsequent increasing regulations, utilities are seeking solutions to reliably continue providing energy to customers. Machine learning technology is now more accessible for companies of all sizes and using it to manage weather impacts is a smart – and necessary – investment for smaller utilities. Decision-makers will have increased capability to make agile, confident decisions in the moment about keeping communities and infrastructure safe, while maintaining operations and reducing risk.

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