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Electrical and electronic engineering
Data centers account for approx. 1.4% of the world’s electricity consumption, of which up to 50% of that power is dedicated to keeping the actual equipment cool. This represents a huge opportunity to reduce data center energy consumption by tackling the cooling system operations with focus on thermal management. This work presents a novel Data Driven Predictive Model (DDPM) for temperature prediction of server inlet temperatures that utilises high resolution empirical temperature measurements from 52 real-life data centers. A knowledge-base of temperature data and related physical features, created via clustering techniques was used to train a series of artificial neural networks (ANN). The ANNs are used to make predictions of server inlet temperatures based on inputs which describe the boundary conditions. The temperature predictions are made for each server rack to estimate the vertical temperature distribution (s-curve) from the bottom to top of the rack spaced at one foot intervals. Each ANN predicts a temperature at a corresponding vertical height for the given inputs, producing the s-curve reconstructed from the combination of ANN outputs. Furthermore, one ANN predicts the s-curve cluster which is used to provide a prediction confidence.
Lloyd, R. & Rebow, M. (2018) Data Driven Prediction Model (DDPM) for Server Inlet Temperature Prediction in Raised-floor Data Centers, 2018 17th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm) DOI:10.1109/itherm.2018.8419650