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Digital Twin for Tuning of Server Fan Controllers
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0003-4293-6408
RISE Research Institutes of Sweden.
RISE Research Institutes of Sweden.
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0002-9759-5594
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2019 (English)In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, p. 1425-1428Conference paper, Published paper (Refereed)
Abstract [en]

Cooling of IT equipment consumes a large proportion of a modern data centre’s energy budget and is therefore an important target for optimal control. This study analyses a scaled down system of six servers with cooling fans by implementing a minimal data driven time-series model in TensorFlow/Keras, a modern software package popular for deep learning. The model is inspired by the physical laws of heat exchange, but with all parameters obtained by optimisation. It is encoded as a customised Recurrent Neural Network and exposed to the time-series data via n-step Prediction Error Minimisation (PEM). The thus obtained Digital Twin of the physical system is then used directly to construct a Model Predictive Control (MPC) type regulator that executes in real time. The MPC is then compared in simulation with a self-tuning PID controller that adjust its parameters on-line by gradient descent.

Place, publisher, year, edition, pages
2019. p. 1425-1428
Series
IEEE International Conference on Industrial Informatics (INDIN)
Keywords [en]
RNN, PEM, TensorFlow, MPC, Digital Twin
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:ri:diva-64221DOI: 10.1109/INDIN41052.2019.8972291OAI: oai:DiVA.org:ri-64221DiVA, id: diva2:1742678
Conference
2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
Note

ISBN för värdpublikation: 978-1-7281-2927-3, 978-1-7281-2928-0

Available from: 2023-03-10 Created: 2023-03-10 Last updated: 2023-06-07Bibliographically approved

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Brännvall, RickardGustafsson, JonasSummers, Jon

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