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Current members

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  • Team
  • Current members
  • Peter Atrazhev (MSc student)
  • Tomas Barton (PhD candidate)
  • Nastaran Gholizadeh (PhD student)
  • Nazli Kazemi (PhD student)
  • Elizaveta Kharlova (MSc student)
  • Yashar Kor (PhD candidate)
  • Bahareh Lashkari (PhD student)
  • Daniel May (PhD candidate)
  • Carolina Quiroz Juarez (MSc student)
  • Mikael Sabuhi (PhD student)
  • Alyssa Schatz (BA intern)
  • Justin Schlauwitz (PhD candidate)
  • Kalvin Schofield (MSc student)
  • Steven Zhang (PhD candidate)
  • Chloe Zhou (MSc student)

Recent Posts

  • Article “Optimal Design of Distribution Overhead Powerlines using Genetic Algorithms” was accepted for publication in IEEE Transactions on Power Delivery
  • Article “Dimension-wise Particle Swarm Optimization: Evaluation and Comparative Analysis” was published in MDPI Applied Sciences
  • Article “Distributed Learning Applications in Power Systems: Methods, Gaps, and Challenges” has been published in MDPI Energies
  • Article “Probabilistic Forecasting of Dynamic Thermal Line Rating with Temporal Correlations” was accepted to the International Journal of Electrical Power and Energy Systems
  • Article “Comparative Analysis of Machine Learning ‎Techniques for Temperature Compensation in ‎Microwave Sensors” was accepted for publication in IEEE Transactions on Microwave Theory and Techniques

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About Us

The ENergy digiTizAtIon Lab is concerned with the digitization of electrical energy systems across all levels, both horizontally and vertically: from IoT devices providing access to sensors and levers of the smart grid to cloud-based services for storing and processing the large amount of data produced by the modern electricity grids.

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Recent Posts

  • Article “Optimal Design of Distribution Overhead Powerlines using Genetic Algorithms” was accepted for publication in IEEE Transactions on Power Delivery
  • Article “Dimension-wise Particle Swarm Optimization: Evaluation and Comparative Analysis” was published in MDPI Applied Sciences
  • Article “Distributed Learning Applications in Power Systems: Methods, Gaps, and Challenges” has been published in MDPI Energies
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