most citedExploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector

137 citations · 154 across the 5 of their papers we have counts for

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5 papers

eess.SY2026

Workload composition smooths aggregate power demand while sustaining short-horizon ramps in AI data centers

Subir Majumder, Minlan Yu, Le Xie

Artificial intelligence (AI) is driving rapid growth in electricity demand, yet the grid-facing power dynamics of AI data centers remain poorly understood. Here we show that, in sh…

eess.SY2024

An Econometric Analysis of Large Flexible Cryptocurrency-mining Consumers in Electricity Markets

Subir Majumder, Ignacio Aravena, Le Xie

In recent years, power grids have seen a surge in large cryptocurrency mining firms, with individual consumption levels reaching 700MW. This study examines the behavior of these fi…

eess.SY2024★ 1 cited

Predicting DC-Link Capacitor Current Ripple in AC-DC Rectifier Circuits Using Fine-Tuned Large Language Models

Mohamed Zeid, Subir Majumder, Hasan Ibrahim +3

Foundational Large Language Models (LLMs) such as GPT-3.5-turbo allow users to refine the model based on newer information, known as ``fine-tuning''. This paper leverages this abil…

eess.SY2024★ 16 cited

The Role of Electric Grid Research in Addressing Climate Change

Le Xie, Subir Majumder, Tong Huang +4

Addressing the urgency of climate change necessitates a coordinated and inclusive effort from all relevant stakeholders. Critical to this effort is the modeling, analysis, control,…

eess.SY2024★ 137 cited

Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector

Subir Majumder, Lin Dong, Fatemeh Doudi +7

Large Language Models (LLMs) as chatbots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. Wh…