Creation of a Screening Analytical Approach for the Efficient Detection of Anomalous Performance across Large Refrigeration Pack Estates Using Electrical Usage Data

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


This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.


Questions about this service can be mailed to .