Using Machine Learning / AI can help prevent being caught off guard by unexpected outages, particularly when storms become severe.
Category: Weather
Hurricane Ian and the Power of Data
Learn how data-driven weather insights and machine learning can help utilities minimize cost and disruption when hurricanes strike.
Decoding Weather Data for Strategic Decision-Making
Why using data-driven weather intelligence and machine learning is helping utilities make agile, confident decisions to mitigate disruption.
Learning from the Blue Blizzard: Mitigating Snowstorm Impact on Utilities
Utility companies can learn how to plan ahead and minimize disruption caused by extreme snow storm events by looking at the Blue Blizzard example.
Ice Storm Mara: Lessons and Resilience Strategies for Utility Companies
Utility companies can learn the lessons from how storms have disrupted supply in the past. We look at 2023’s biggest ice storm an explain how this event can be used to inform future resilience planning.
Lessons Learned from Winter Storm Uri: Building Resilience in the Face of Extreme Cold
How utility companies can improve preparedness for the challenges of prolonged cold temperatures by looking at a past example.
The 2023 Atlantic Hurricane Season: Hot Water Fuels More Monsters
Due to the significant impacts, hurricane seasons are met with concern and uncertainty. Learn about the 2023 season and recent trends around intense storms.
How Extreme Weather Disrupts the Oil and Gas Sector
The weather has significant effects on the entire oil and gas supply chain. See how different extreme weather events disrupt the sector.
The Weather Intelligence Advantage in the Mining Industry
Discover how exceptional weather intelligence can optimize your mining operations every day and increase your site’s resilience for the future.
Lightsource bp Leverages DTN Weather Intelligence to Reduce Risks
The solar industry is growing, and with it, the potential for weather risks. Find out how Lightsource bp uses weather intelligence to reduce hailstorm loss.