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DTSTAMP:20230831T095755Z
LOCATION:Sanada I
DTSTART;TZID=Europe/Stockholm:20230628T110000
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UID:submissions.pasc-conference.org_PASC23_sess156@linklings.com
SUMMARY:MS5G - Hybrid Energy Systems: Adaptive Computing to Harmonize Ener
 gy Generation and Demand for Communities across Spatio-Temporal Scales
DESCRIPTION:Minisymposium\n\nData-driven computational approaches that bri
 ng computing and storage nearer to the requests, and especially operating 
 on instant real-time sensor data, have contributed immensely to our state 
 of knowledge of the impacts of climate change on our built environment and
  human systems. The holistic understanding of these issues demand a cross-
 disciplinary approach that is rigorous, based on data, and embedded in the
  domain. This mini symposium focuses on bringing together experts and rese
 archers in edge-computing with knowledge of the driving issues for a discu
 ssion on the existing challenges and advances needed in driving edge-compu
 ting deployments towards well understood goals of sustainability, resilien
 ce, and decarbonization. The international panel of invited speakers will 
 cover a wide range of topics, from a deeply data and computational perspec
 tive about IoT, streaming data, and real-time analytics, to traceability o
 f the data sources for deep decarbonization and their impact on policy-ene
 rgy-economics. Issues of data scarcity and of inequity in the Global South
  will be discussed. We expect to round off the discussion with a forward-l
 ooking view of integrated communities of the future as highly controllable
  entities in which the built environment - buildings, vehicles, energy gen
 eration, and the grid, collectively maximize the user’s wellbeing.\n\nData
  Analytics for Resilient Energy Infrastructure and Communities\n\nResilien
 ce of our energy infrastructure under weather extremes is an important pro
 blem to the US and the world as shown by large-scale power outages occurre
 d in the past 10 years. Resilience is to reduce the impact of such power d
 isruptions to people, which is particularly important in a changing cl...\
 n\n\nChuanyi Ji (Georgia Institute of Technology)\n---------------------\n
 Swarm Learning ~ Distributed ML for Edge Adaptive Computing\n\nMachine lea
 rning (ML) has enjoyed success in applications like sensor detection and o
 bject identification . In a centralized ML approach, the training data is 
 aggregated in a centralized location, where machine learning models are de
 veloped, trained, and tested. However, the centralized approach is ...\n\n
 \nBill Burnham (HPE)\n---------------------\nMulti-Scale Multi-Domain Mode
 ling of Net Zero Energy Community\n\nThis presentation introduces our rece
 nt research in developing the Modelica-based multi-scale multi-domain mode
 ling technology for the optimal design and operation of net zero energy co
 mmunities (NZEC). Based on a real-world NZEC in Florida, USA, an open sour
 ce Modelica NZEC library has been develop...\n\n\nWangda Zuo (University o
 f Pennsylvania)\n---------------------\nAdvanced Computing for Hybrid Ener
 gy Systems\n\nHybrid Energy Systems sit at the nexus of energy generation,
  storage, and end-use. With increasing penetration of renewables in the ge
 neration infrastructure, single technology solutions are increasingly unab
 le to meet the energy supply consistency and/or grid service needs. Combin
 ations of technolo...\n\n\nJibonananda Sanyal (National Renewable Energy L
 aboratory)\n\nDomain: Computer Science, Machine Learning, and Applied Math
 ematics 
\n\nSession Chair: Jibonananda Sanyal (National Renewable Energy 
 Laboratory)
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