Session Category: Advanced Analytics

The world is being transformed each day though advanced analytics and data science. This track examines the new approaches to data science that a growing number of utilities are employing for predictive analytics, optimization, and machine learning.

May 7, 2019

10:00  -  11:00

Grand Ballroom B

103: Identify Asset Remaining Life Using Advance Analytics and Machine Learning

Dubai electricity and water authority’s distribution network has an intensive portfolio of assets, with all the related cost, risk & performance involved in the whole life cycle management process. Nowadays, Distribution Asset Management system is comforting an uphill challenge, with continual increase in an already huge asset portfolio. Challenges, resulted by the ageing infrastructure and … Continued

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303: Vegetation Prioritization meets 1-km Spatial Analytics of Overhead Electric Lines and Satellite Imaging

2:00  -  3:00

Tryon

303: Vegetation Prioritization meets 1-km Spatial Analytics of Overhead Electric Lines and Satellite Imaging

CPS Energy spends hundreds of thousands of dollars in directing field crews to trim trees in the areas of potentially biggest impact to the electric grid reliability. This action is both pro-active and re-active as our vegetation management crews could not physically inspect all circuit lines across our service territory at a speed and frequency … Continued

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403: Rewriting the Story with Advanced Analytics

3:15  -  4:30

Harris

403: Rewriting the Story with Advanced Analytics

Our expert panel will share their perspectives on how the world is transforming with the use of advanced analytics and data science, including a focus on the cutting-edge ways in which utilities are employing predictive analytics, optimization, and machine learning. Come and share your experiences during Q&A or get a glimpse of the future as … Continued

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May 8, 2019

11:30  -  12:30

Grand Ballroom B

703: Utilizing Machine Learning to Predict Outage Complaints at DTE

Improving customer satisfaction for distribution system reliability is possible through Machine Learning and Data Analytics. Predicting the likelihood for an individual customer to complain, is achievable, by applying data science to the abundant of information available through AMI and historically collected data. This session will explore DTE Energy’s machine learning lifecycle with forecasting frequent outage … Continued

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