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New Outage Prediction Model From The Weather Company, an IBM Business, Helps Utilities Prepare For and Respond to Severe Weather

The Weather Company, an IBM Business (NYSE: IBM), introduced a new outage prediction solution designed to help utility companies proactively prepare for and respond to weather-related outages. Tailored to each utility’s storm response plan, The Weather Company’s Outage Prediction solution helps predict the anticipated number of outages and appropriate mobilization level based on the weather forecast for the utility’s service territory. This enables the utility’s operations team to make critical decisions as much as 72 hours ahead of an anticipated weather event – helping them to control costs and improve restorations times for their customers.

With our new Outage Prediction solution, storm operations and emergency management teams are now able to make more informed decisions to improve restoration times, and save utility companies millions of dollars per year,” said Mark Gildersleeve, president of business solutions, The Weather Company. “This solution makes a huge impact when every hour of advance notice counts.”

The Outage Prediction model offers a three-pronged approach, which will allow utility companies to:

  • Review an automated outage and mobilization prediction model with 72-hour lead time, updated as weather models update in real-time
  • Perform historical storm searches, comparing current weather forecast conditions and predicted outage risks to past outcomes under similar weather conditions
  • Plan for best/worst case scenarios looking at variable factors such as wind speed, precipitation, temperature, and humidity

Utilizing a machine-learning predictive model, Outage Prediction is an intelligent solution that combines historical weather data and the most up-to-date, hyperlocal weather forecasts with past outage and/or infrastructure damage information for a utility’s service area. By combining this information, the utility can see what areas are predicted to be hit the hardest, which is critical in deciding where and when to pre-stage restoration crews and equipment.

The predictive model also accounts for parameters such as atmospheric pressure, soil moisture, and foliage. Optionally, other external attributes like type of vegetation, soil and/or other utility specific attributes like asset details can be incorporated via a services engagement. This flexibility enables an individualized solution for each utility’s situation, to help drive more informed decision making.

 

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