5 citations · 5 across the 5 of their papers we have counts for
5 papers
Smoothing the Ramp, Not the Peak: Scheduling-Induced Power Dynamics of LLM Inference and Their Grid-Scale Consequences
Pan Li, Yize Chen, Xia Miao +1
Large language model (LLM) inference serving is a fast-growing electricity load whose power dynamics remain uncharacterized from a grid-planning perspective. Using real, measured G…
Short-Term Load Forecasting for AI-Data Center
Mariam Mughees, Yuzhuo Li, Yize Chen +1
Recent research shows large-scale AI-centric data centers could experience rapid fluctuations in power demand due to varying computation loads, such as sudden spikes from inference…
Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
Di Shi, Qiang Zhang, Mingguo Hong +4
Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in pow…
The Unseen AI Disruptions for Power Grids: LLM-Induced Transients
Yuzhuo Li, Mariam Mughees, Yize Chen +1
Recent breakthroughs of large language models (LLMs) have exhibited superior capability across major industries and stimulated multi-hundred-billion-dollar investment in AI-centric…
Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition
Yuqi Jiang, Yan Li, Yize Chen
Rapid progress in machine learning and deep learning has enabled a wide range of applications in the electricity load forecasting of power systems, for instance, univariate and mul…