6 citations · 25 across the 18 of their papers we have counts for
5 papers · 1 filter
Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework
Mengshuo Jia, Zeyu Cui, Gabriela Hug
The integration of experimental technologies with large language models (LLMs) is transforming scientific research. It positions AI as a versatile research assistant rather than a…
Zero and Few Shot Load Forecasting with Large Language Models
Wenlong Liao, Chengrui Zhang, Zhe Yang +4
Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, w…
Data-driven Power Flow Linearization: Theory
Mengshuo Jia, Gabriela Hug, Ning Zhang +3
This two-part tutorial dives into the field of data-driven power flow linearization (DPFL), a domain gaining increased attention. DPFL stands out for its higher approximation accur…
Data-driven Power Flow Linearization: Simulation
Mengshuo Jia, Gabriela Hug, Ning Zhang +3
Building on the theoretical insights of Part I, this paper, as the second part of the tutorial, dives deeper into data-driven power flow linearization (DPFL), focusing on comprehen…
Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Daline
Mengshuo Jia, Zeyu Cui, Gabriela Hug
The integration of experiment technologies with large language models (LLMs) is transforming scientific research, offering AI capabilities beyond specialized problem-solving to bec…