Kyle Decker is part of the E Source GridInform team that delivers AI and data science solutions to support decision-making in maintaining and operating the transmission and distribution side of the utility business. While at E Source, he has led and supported data science works to understand and estimate the impact of weather on power infrastructure, both retrospectively and to forecast future impacts; predict estimated outage restoration times; quantify asset health for myriad critical power infrastructure components; identify and characterize infrastructure from aerial imagery; and detect specific appliance usage profiles within advanced metering infrastructure data (for example, EV charging). Prior to joining E Source, Kyle was a data scientist at Trove Predictive Data Science, working on similar applications within the utility sector. Ahead of working in the utility industry, Kyle spent several years developing image processing and computer vision techniques for a wide variety of applications in both the manufacturing and medical imaging domains. He earned MS and BS degrees in biomedical engineering from Duke University and University at Buffalo, respectively.
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How Hurricane Idalia can inspire utilities to take a data-driven approach to outage prediction
October 19, 2023
Late this summer, the Southeast US was hit by Hurricane Idalia. While not every storm is an “all hands on deck” situation like Idalia, the hurricane should inspire utilities to implement a data-driven solution to receive the same level of prediction accuracy year-round.