Historical analysis of electrical energy usage data from over 350 High Temperature and Low Temperature retail refrigeration packs has shown a very high level of variability in pack electrical energy usage. This noisy big data environment makes the task of detecting anomalous pack behaviour energy wastage events, a very difficult one. This paper attempts to address this problem, by presenting a modified process characterization approach that through the meaningful subcategorisation and statistical analysis of a pack’s annualized energy usage, can give the practitioner a much better understanding of the relative contributions of baseload, within day variation, and summer seasonal variation sources, present within a pack. It goes on to show that the resultant creation of a screening analytic using only electrical usage data, when applied across a complete estate, can deliver effective pack level anomaly detection, and subsequent cost savings through the timely detection and avoidance of these significant energy wastage events.
By: N. Brady, J. Walsh
Published in: RC25579 in 2015
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