Pith. sign in

REVIEW 1 cited by

Towards an AI assistant for power grid operators

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2012.02026 v2 pith:JU23CYNG submitted 2020-12-03 stat.ML cs.AIcs.LG

classification stat.MLcs.AIcs.LG
keywords assistantinterfacedecision-makinghuman-machinemakepowertowardsadvantage
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Power grids are becoming more complex to operate in the digital age given the current energy transition to cope with climate change. As a result, real-time decision-making is getting more challenging as the human operator has to deal with more information, more uncertainty, more applications, and more coordination. While supervision has been primarily used to help them make decisions over the last decades, it cannot reasonably scale up anymore. There is a great need for rethinking the human-machine interface under more unified and interactive frameworks. Taking advantage of the latest developments in Human-Machine Interface and Artificial Intelligence, we expose our vision of a new assistant framework relying on an hypervision interface and greater bidirectional interaction. We review the known principles of decision-making driving our assistant design alongside with its supporting assistance functions. We finally share some guidelines to make progress towards the development of such an assistant.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Machine Learning for Physical Simulation Challenge Results and Retrospective Analysis: Power Grid Use Case

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A competition retrospective shows that hybrid AI/physics solvers can speed up power flow computation several-fold while remaining physically plausible, though the top speed-up of 7.87x missed the stated 10x target.

Pith tools