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Short-Term Load Forecasting for AI-Data Center
This is an IEEE and RCES online seminar.
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 or interruption of training large language models (LLMs). As a consequence, such huge and fluctuating power demand pose significant challenges to both data center and power utility operation. Accurate short-term power forecasting allows data centers and utilities to dynamically allocate resources and power large computing clusters as required. However, due to the complex data center power usage patterns and the black-box nature of the underlying AI algorithms running in data centers, explicit modeling of AI-data center is quite challenging.
Alternatively, to deal with this emerging load forecasting problem, we propose a data-driven workflow to model and predict the short-term electricity load in an AI-data center, and such workflow is compatible with learning-based algorithms such as LSTM, GRU, 1D-CNN. We validate our framework, which achieves decent accuracy on data center GPU short-term power consumption. This provides opportunity for improved power management and sustainable data center operations.
Date and Time
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Location
- This event has virtual attendance info. Please visit the event page to attend virtually.
Google Meet joining info
Video call link: https://meet.google.com/pdu-shhr-ubc
Or dial: (CA) +1 587-797-9950 PIN: 729 003 043#
More phone numbers: https://tel.meet/pdu-shhr-ubc?pin=3592737696415
Hosts
- Northern Canada Section Chapter,PEL35
- Northern Canada Sect Aff Grp,YP
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- Co-sponsored by Resilience and Clean Energy Systems (RCES)
Registration
- Starts 11 November 2025 12:00 AM MST
- Ends 21 November 2025 12:00 PM MST
- No Admission Charge
Speakers
Mariam Mughees, PhD student from University of Alberta
Short-Term Load Forecasting for AI-Data Center
Mariam Mughees is a PhD student in the Department of Electrical and Computer Engineering at the University of Alberta, where she has been enrolled since September 2022. Her research focuses on investigating data centre power consumption challenges caused by large language models (LLMs) and its management using machine learning–based methods . Before starting her PhD, she worked as a Lecturer in the Electrical Engineering Department at UET Lahore, Pakistan.
