# IEEE Power & Energy Magazine - May/June 2018 - 20

```1549

Demand (kWh)

1539

the other hand, depending upon the
thickness of the percentiles on a
6
plot, they may be overlapping each
4
other, indicating that the observations are close to each other. in
2
the extreme case where all obser0
vations are identical, the percen6
tiles are identical too.
overlaid on the individual de4
mand data, figure 2 also shows
2
some percentiles of the demand
distributions as they vary by half
0
hour and day of the week, allow0
200
400
Days
ing us to see 48 # 7 = 336 probability distributions per household. for some periods, such as
figure 1. Two examples of smart-meter demand from the CER data set.
early morning around 4 a.m. for
meter 1549, the selected percentiles are indistinguishable, indiPercentiles by Time of Week
cating
similar
levels
because electricity consumption
one way to see intraday and intraweek patterns is to plot the
demand against the time of the week, rather than against the during sleeping hours is low and relatively certain. the
time since the beginning of data collection. figure 2 shows evening hours (e.g., hours 18-24) are showing widespread
the same data displayed in figure 1 but as a scatter plot percentiles, as the result of varying electricity consumpagainst the time of the week. now, the morning and evening tion activities.
the percentiles are smoothed a little over time by compeaks for meter 1539 become clear, and it is also apparent
that meter 1549 has a different pattern on weekends than bining neighboring half hours. for example, the percentiles for half-hour h are estimated using the data for halfon weekdays.
to further examine the intraday load profiles, we can lever- hours h - 1, h, and h + 1. this is equivalent to a form of
age the concept of percentile, which describes the distribution kernel smoothing across half hours. We use a simple estiof the observations. the tenth percentile, for example, is the mate of each percentile to compute these curves. in this
value below which 10% of the observations may be found. context, simple estimates are better than kernel density
Widespread percentiles indicate widespread observations. on estimates (or some other more sophisticated estimate of the

Monday

Tuesday

6 12 18 24 0

6 12 18 24 0

Wednesday

Thursday

Friday

Saturday

Sunday

6 12 18 24 0

6 12 18 24

6
1539

2
0
6
4

1549

Demand (kWh)

4

2
0
0

6 12 18 24 0

Percentile

10

6 12 18 24 0
Time of Day
25

50

6 12 18 24 0

75

90

figure 2. The demand plotted against the time of the week for two smart meters from the CER data set.
20

ieee power & energy magazine

may/june 2018

```

# 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
IEEE Power & Energy Magazine - May/June 2018 - 3
IEEE Power & Energy Magazine - May/June 2018 - 4
IEEE Power & Energy Magazine - May/June 2018 - 5
IEEE Power & Energy Magazine - May/June 2018 - 6
IEEE Power & Energy Magazine - May/June 2018 - 7
IEEE Power & Energy Magazine - May/June 2018 - 8
IEEE Power & Energy Magazine - May/June 2018 - 9
IEEE Power & Energy Magazine - May/June 2018 - 10
IEEE Power & Energy Magazine - May/June 2018 - 11
IEEE Power & Energy Magazine - May/June 2018 - 12
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IEEE Power & Energy Magazine - May/June 2018 - 14
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IEEE Power & Energy Magazine - May/June 2018 - 20
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IEEE Power & Energy Magazine - May/June 2018 - 108
IEEE Power & Energy Magazine - May/June 2018 - Cover3
IEEE Power & Energy Magazine - May/June 2018 - Cover4
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