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DTSTAMP:20241227T190834Z
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DTSTART;TZID=America/Los_Angeles:20241210T110000
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DESCRIPTION:The combined Santa Clara Valley\, San Francisco\, & Oakland/Eas
 t Bay IEEE PELS is very pleased to invite you to our upcoming online webin
 ar. We are excited and honored to have Prof. Qianwen Xu of the KTH Royal I
 nstitute of Technology\, Sweden to speak on the topic of AI and Digitaliza
 tion as Enablers of the Clean Energy Transition.\n\nHere's more about the 
 talk from the Speaker:\n\nAs we transition towards a climate-neutral socie
 ty\, future energy systems will increasingly feature high shares of renewa
 ble energy sources and power electronic converters. However\, these advanc
 ements introduce significant challenges in maintaining power balance and s
 ystem stability. This talk will explore how AI helps to address these chal
 lenges. Specifically\, the talk focuses on transfer learning based modelin
 g for converter interfaced assets\, digital twin development of power syst
 ems\, and safe deep reinforcement learning for grid optimization with nume
 rous converter interfaced renewable energy sources. This talk targets thos
 e interested in tech-driven environmental sustainability\, offering insigh
 ts into how digital advances support the clean energy transition.\n\nVirtu
 al: https://events.vtools.ieee.org/m/443097
LOCATION:Virtual: https://events.vtools.ieee.org/m/443097
ORGANIZER:haravesudarshan@ieee.org
SEQUENCE:18
SUMMARY:AI and Digitalization as Enablers of the Clean Energy Transition
URL;VALUE=URI:https://events.vtools.ieee.org/m/443097
X-ALT-DESC:Description: <br /><p>The combined Santa Clara Valley\, San Fran
 cisco\, &amp\; Oakland/East Bay IEEE PELS is very pleased to invite you to
  our upcoming online webinar. We are excited and honored to have Prof. Qia
 nwen Xu of the KTH Royal Institute of Technology\, Sweden to speak on the 
 topic of AI and Digitalization as Enablers of the Clean Energy Transition.
 </p>\n<p>Here's more about the talk from the Speaker:</p>\n<p>As we transi
 tion towards a climate-neutral society\, future energy systems will increa
 singly feature high shares of renewable energy sources and power electroni
 c converters. However\, these advancements introduce significant challenge
 s in maintaining power balance and system stability. This talk will explor
 e how AI helps to address these challenges. Specifically\, the talk focuse
 s on transfer learning based modeling for converter interfaced assets\, di
 gital twin development of power systems\, and safe deep reinforcement lear
 ning for grid optimization with numerous converter interfaced renewable en
 ergy sources. This talk targets those interested in tech-driven environmen
 tal sustainability\, offering insights into how digital advances support t
 he clean energy transition.</p>
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