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DTSTAMP:20230831T095747Z
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DTSTART;TZID=Europe/Stockholm:20230628T120000
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UID:submissions.pasc-conference.org_PASC23_sess156_msa261@linklings.com
SUMMARY:Data Analytics for Resilient Energy Infrastructure and Communities
DESCRIPTION:Minisymposium\n\nChuanyi Ji (Georgia Institute of Technology)\
 n\nResilience of our energy infrastructure under weather extremes is an im
 portant problem to the US and the world as shown by large-scale power outa
 ges occurred in the past 10 years. Resilience is to reduce the impact of s
 uch power disruptions to people, which is particularly important in a chan
 ging climate. A fundamental question is how resilient is our energy infras
 tructure to weather extremes? Further, how does enhancing the resilience o
 f the energy infrastructure benefit people? We describe how data analytics
  plays an important role in studying resilience. We collect field data fro
 m the operational distribution grid. The data are at the large scale, span
 ning multiple service regions across the US states. We then describe how c
 omputational algorithms based on spatial temporal models are derived to le
 arn knowledge from the data. The analytics disseminates knowledge, i.e., i
 nsights on vulnerability and potential improvements of the energy infrastr
 ucture. We discuss a future direction on extending resilience study to com
 munities through Georgia EnergyShed (G-Shed)*. <br />*Joint work with 12 c
 o-authors from Georgia Tech, New York State utilities and Public Service C
 ommission. <br />*G-Shed: a DoE team-project led by Richard Simmons at Geo
 rgia Tech.\n\nDomain: Computer Science, Machine Learning, and Applied Math
 ematics &#8232;\n\nSession Chair: Jibonananda Sanyal (National Renewable Energy 
 Laboratory)
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