Artificial Intelligence Meets Effective Electricity: Deep Learning for Power Systems Analytics
Deep learning (DL)/machine learning (ML) is one of the areas within Artificial intelligence (AI). DL has lately drawn considerable research as well as industrial attention as it enables automatically learning system knowledge when a large amount of training data is available. In computer vision, DL has reached or even exceeded human performance. The aim of this work package is to exploit interdisciplinary research opportunities between DL and power system engineering. More specifically, the aim is to: a) Identify those subjects of interest to the power engineering division at E2 where
AI/DL/ML technology can be exploited; b) Identify possible collaborations between different research areas within Chalmers;
c) Organize a short course for senior researchers who need to gain basic knowledge and hands-on approaches to set up future research collaborations applying ML/DL to their research areas;
d) Conduct some small case studies for DL applications, e.g. DL analytics for understanding/ improving electricity consumption, efficient usage of rechargeable batteries for electrical vehicles; voltage stability impact of renewable electricity resources. Tasks (a) and (b) will be realized by organizing discussion meetings, workshop, seminars as well as meetings with relevant industrial partners. From the above, the short course in (c) will be delivered to those who are interested, focusing on power systems and energy-related ML basics and hands-on learning. Furthermore, some master thesis projects will be defined for pilot study of identified subjects, also some potential PhD research/projects. We also plan to do some case studies on DL from the candidate list in (d).
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