IEEE Power & Energy Magazine - May/June 2018 - 18

Visualizing
Big Energy
Data
Solutions for This Crucial
Component of Data Analysis

By Rob J. Hyndman,
Xueqin (Amy) Liu,
and Pierre Pinson

V

Visualization is a crucial component of
data analysis. it is always a good idea to plot the data before
fitting models, making predictions, or drawing conclusions.
as sensors of the electric grid are collecting large volumes
of data from various sources, power industry professionals
are facing the challenge of visualizing such data in a timely
fashion. in this article, we demonstrate several data-visualization solutions for big energy data through three case studies
involving smart-meter data, phasor measurement unit (pmu)
data, and probabilistic forecasts, respectively.

Digital Object Identifier 10.1109/MPE.2018.2801441
Date of publication: 18 April 2018

18

ieee power & energy magazine

Visualizing Smart-Meter Data
smart grid initiatives worldwide have deployed millions of
smart meters into the electric grid. a small-to-medium-sized
utility company could have thousands of meters spread across
its territory, recording electricity demand at hourly or subhourly intervals. But how should one actually plot data on
thousands of smart meters, with each comprising thousands of
observations over time? We cannot simply produce time plots
of the demand recorded at each meter, due to the sheer volume
of data involved.
one approach is to convert each long series of demand
data to a single two-dimensional (2-D) point that can be plotted in a simple scatter plot. in that way, all the meters can be
seen in the scatter plot; outliers can be detected, clustering

1540-7977/18©2018IEEE

may/june 2018



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

Contents
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