REVIEW 4 cited by
Large Foundation Models for Power Systems
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
read the original abstract
Foundation models, such as Large Language Models (LLMs), can respond to a wide range of format-free queries without any task-specific data collection or model training, creating various research and application opportunities for the modeling and operation of large-scale power systems. In this paper, we outline how such large foundation model such as GPT-4 are developed, and discuss how they can be leveraged in challenging power and energy system tasks. We first investigate the potential of existing foundation models by validating their performance on four representative tasks across power system domains, including the optimal power flow (OPF), electric vehicle (EV) scheduling, knowledge retrieval for power engineering technical reports, and situation awareness. Our results indicate strong capabilities of such foundation models on boosting the efficiency and reliability of power system operational pipelines. We also provide suggestions and projections on future deployment of foundation models in power system applications.
Forward citations
Cited by 4 Pith papers
-
Agentic Artificial Intelligence for Power Systems: Strategies to Identify and Close Capability Gaps
A benchmark of agentic AI for power-system planning finds current-style agents only solve the two simplest task levels on small grids and fail on larger grids or harder tasks.
-
LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration
A fine-tuned LLaMA-3.1 model with a custom loss function can emulate a reference reconfiguration algorithm on small IEEE test feeders, but it is slower than a stochastic optimizer and generalizes poorly to a 136-bus u...
-
Large Language Model Interface for Home Energy Management Systems
A ReAct and few-shot prompted LLM agent retrieves well-formatted HEMS parameters from natural-language user replies with 88% accuracy in LLM-simulated tests.
-
Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial
A survey and tutorial that organizes LLM-enabled wireless network optimization into formulation, solution, and verification stages, with case studies drawn from the authors' own prior papers.
Discussion (0). Continue with ORCID to comment.