Home
Research group
Publications
Blog
Students

Interested in joining our team?

Home
Interested in joining our team?

Contact us if you are interested in joining ENTAIL lab. Please note that we only respond to applicants selected for an interview.

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

Categories

Articles
Brochure
Engagement
Grants
Projects

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.

Categories

Articles
Brochure
Engagement
Grants
Projects

Menu

Research
Blog
Human Resources
Publications
Team

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

© Copyright 2020 ENTAIL

WordPress Theme | Hashone by Hash Themes