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Tuesday, March 20 • 3:35pm - 4:05pm
Forecasting the storm recovery resource allocation in electric utility network
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A leading cause of power outages in the US is weather driven equipment failure events. Oracle utility customers are seeking machine learning techniques (recommended by the USDOE) to improve real-time risk assessment for critical assets based upon incoming weather forecast. Given an accurate prediction for the location, type, and amount of the storm-damaged equipment, the utility company can position the right number of specialized crews and material resources ahead of time. We develop a novel machine-learning model trained on the history of asset failures and reported weather. Since the weather-reporting stations might be far from the assets and some weather data measurements might be missing, we interpolate the weather conditions at the asset locations using a triangularization function computed with a barycentric coordinates technique implementation. We report performance comparison of multiple machine learning models to recommend the best damage prediction tool.

avatar for Kenny  Gross

Kenny Gross

Architect, Oracle
Kenny C Gross, PhD, is an AI Architect in Oracle's Physical Sciences Research Center in San Diego, CA. Kenny specializes in advanced pattern recognition and prognostic anomaly discovery for Big Data applications in IoT industries, life sciences, and business business-critical data... Read More →
avatar for Andrew Vakhutinsky

Andrew Vakhutinsky

Consulting Member of Technical Staff, Oracle
Andrew Vakhutinsky is a Consulting Member of Technical Staff at Oracle Labs. He has Ph.D. in Operations Research from University of Maryland, College Park, and M.Sc. in Physics and Applied Mathematics from Moscow University of Physics and Technology. Andrew's previous experience includes... Read More →

Tuesday March 20, 2018 3:35pm - 4:05pm PDT
4-Rm 104