IEEE Power & Energy Magazine - May/June 2018 - 19

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can be observed, and any other interesting structure can be
examined. We will present a solution to this problem by first
converting the data from each smart meter into a series of
probability distributions, which are then used to compute
pairwise distances between load profiles. the households
are embedded in 2-D space to enable simple but informative
plots to be constructed.

Irish Smart-Meter Data
to illustrate, we will use data collected during a smart-metering trial conducted by the commission for energy regulation (cer) in ireland. for demonstration purposes, we will
use measurements of half-hourly electricity consumption
gathered from 500 residential consumers over 535 consecumay/june 2018

tive days. every meter provides the electricity consumption
between 14 July 2009 and 31 December 2010. many days in
the series have periods of missing data. the cer data set
does not account for energy consumed by heating and cooling systems. either the households use a different source of
energy for heating, such as oil and gas, or a separate meter is
used to measure consumption due to heating. installed cooling systems are not reported in the study.
Data from two smart meters are shown as time-series
plots in figure 1. While it is obvious that these meters have
very different demand patterns, it is not possible to say much
more; the time-of-day and day-of-week patterns are hidden
due to the volume of data, and even the median demand is
not clear from such plots.
ieee power & energy magazine

19


http://www.iStockphoto.com/z_wei

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2018

Contents
IEEE Power & Energy Magazine - May/June 2018 - Cover1
IEEE Power & Energy Magazine - May/June 2018 - Cover2
IEEE Power & Energy Magazine - May/June 2018 - Contents
IEEE Power & Energy Magazine - May/June 2018 - 2
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IEEE Power & Energy Magazine - May/June 2018 - Cover3
IEEE Power & Energy Magazine - May/June 2018 - Cover4
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