Jonno Bourne worked on a project analyzing smart meter data from several years to help understand customer energy consumption. He used hierarchical clustering and data visualization techniques to analyze the large, messy dataset with over half missing data. This allowed him to identify naturally forming clusters of customers with similar behavior patterns. Once clusters were identified, he could accurately predict what kind of energy user they were and their likely future consumption. The project demonstrated that data science approaches can discover hidden user groups and patterns in smart meter data to improve energy forecasting.

1. JONNO BOURNE
HOW TO TRULY
UNDERSTAND
ENERGY
CONSUMPTION

2. JONNO BOURNE
HOW TO TRULY UNDERSTAND ENERGY CONSUMPTION.
Can Smart meters help us reduce
CO2 by helping us understand
what customers are doing?
The Consultancy group Baringa
reached out to UCL in order to help
them analyse several years worth of
smart meter data. This large data set
had been created by the “Customer
Led Network Revolution” project.
Having previously worked on projects
creating networks out of stock trading
data I immediately saw how a new
type of approach could be applicable
to this problem. My background in
the energy industry supported my
research. After an interview and
discussing various approaches
with Chris Lowry, a consulting data
scientist at Baringa, we got the go
ahead with this exciting project.
Visualisations/Graphs:
These are the results of making a graph on the same day (09-08-
2011) but using different correlation cutoff points - Fig. 1 - uses
a minimum cutoff of 0.9 and so has few connection, the other -
Fig. 2 - has a minimum correlation of 0.7 so has many more.
One of the challenges was that the data was very messy, over half of
it was missing, in order to know which smart meters and time periods
I should use, I needed to find a way to visualise it. The pdf shows
all 153million data points. I used hierarchical clustering to order the
rows and columns by similarity, then aggregated by 5x5 data point
blocks, this allowed me to compress the image to a size that was
manageable for a computer and also made sense to the human eye.
The big red blocks in the middle show very high quality data, its that
area I chose for my research.
Task:
The use of smart meters
and community detection
algorithms has led to the
discovery of naturally
forming clusters of users
that share the same
behaviour patterns.
Once these users are
identified it is possible to
use their ‘Cluster ID’ to
predict what kind of user
they are. From here we
can accurately predict
what cluster they will be
in tomorrow. This will greatly
improve the accuracy of day
ahead prediction.
Review:
At present 20 different
behaviours have
been identified as well
as the identification of
an index of “loyalty”
of customers attached
to each cluster. The next
stage will be to predict
what MOSIAC class the
customers fall into based
on their cluster behaviour
and beyond that apply
the data to use day ahead
prediction for electricity
consumption or volatility.
It is one of the first times
a data science approach
has been applied in
this way, allowing for
the discovery of user
groups that may not have
otherwise been identified.
Fig. 2
Fig. 1

3. Q&A
WE SAT DOWN WITH JONNO AND ASKED HIM A FEW
QUESTIONS ABOUT HIS PROJECT AND ASK WHAT HE
THINKS THE FUTURE HOLDS FOR HIMSELF.
What makes this project unique?
Smart meters have been
researched in various ways but this
is the first time there has been this
type of data science approach
to their use. Using the network
structure allows the discovery of
previously unnoticed natural groups
of users.
What’s the next project
your working on?
I would like to apply a similar
technique to explore and if
possible measure neighbourhood
gentrification through the
movement of ethnic minorities.
What was the moment which
made you realise you wanted to
do what you are doing today?
A year doing online courses, in
statistical programming, made
me realise I wanted to learn at a
deeper level. The next step was
an Msc at UCL.
What is your biggest
career highlight?
Completing my thesis!
What advice would you
give your 18 year old self?
Don’t worry if you don’t know
what you want to do with your
life, just focus on what you want
to do next
Who’s your career hero?
The Black Bear! Flexible and
creative, these guys can excel in
almost any environment - they also
eat a lot of salmon and berries - I
call that a win.
What motto do you
live your life by?
I snowboard and sometimes
people ask, “ do you still fall
over?” I always say, “if you’re not
falling over you’re not trying hard
enough.” I think that is a pretty
good motto for life.
What’s one big trend emerging
in engineering, and why is it
important?
Sensors - everywhere! Sensors
are getting cheaper and smaller
and they can be installed in more
and more equipment allowing it to
communicate. We are creating the
‘Internet of Things’.
What excites you about
the opportunities with
data today and in the future?
Although there is a lot of
data available, there is a lack
of understanding of how to use
it. There are amazing opportunities
to go into dark pools of unused
data and create real value
for society.
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“if you’re
not falling
over...
you’re not
trying hard
enough”