From the 1 of 11 linked papers with an AI index.
11 papers
LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications
Daniela Rojas, Abdulwahab Albassam, Aidan G. Leung +11
Large language models (LLMs) and agentic AI systems have evolved from natural language tasks to using external tools to plan, retrieve, and act in technical domains. In smart grids…
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov +6
The paper surveys classical, machine‑learning, and physics‑informed system identification methods that incorporate control‑relevant properties such as dissipativity and symmetry, d…
RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning
Yuexin Bian, Jie Feng, Tao Wang +3
On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies,…
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
Yuan Zhuang, Yuexin Bian, Sihong He +7
Scaling critic capacity is a promising direction for improving off-policy reinforcement learning (RL). However, recent work shows that larger critics are prone to overfitting and i…
Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting
Sunki Hong, Jisoo Lee
Selecting the right deep learning model for power grid forecasting is challenging, as performance heavily depends on the data available to the operator. This paper presents a compr…
Efficient Policy Adaptation for Voltage Control Under Unknown Topology Changes
Jie Feng, Yuanyuan Shi, Deepjyoti Deka
Reinforcement learning (RL) has shown great potential for designing voltage control policies, but their performance often degrades under changing system conditions such as topology…