FOCUS ENERGY MANAGEMENT
EMISSION MEASURES
The estimated 1.5 million
industrial and commercial (I&C)
buildings in England and Wales
account for around one third of all
UK carbon emissions from total
building stock. But there has been
criticism that the government’s
energy strategy has done little
to incentivise building energy
performance.
The good news is that there
are now digital tools available
to help managers bear down on
consumption. Moreover, the value of
this data will only increase with the
Market-wide Half-Hourly settlement
(MWHH) reform due by 2025, which
will enhance opportunities for
demand side response incentives and
preferential time of use tari s.
The question for energy and
sustainability managers is where best
to invest time and resources to bear
down on energy consumption and
reduce carbon emissions?
At one level, this means optimising
energy metering, consumption
monitoring and data insights, at
another it means applying advanced
technologies, such as machine
learning and artificial intelligence
to drive out waste. Getting all these
systems working in harmony is critical
to optimising energy e iciency and
reducing carbon emissions.
For managers, this means:
Capturing consumption data in
granular detail via automated
meter reading systems
Monitoring and analysing data
through advanced AM&T portals ,
and setting automated alerts for
unusual patterns of behaviour
Applying advanced tools such as
machine learning and artificial
intelligence to identify e iciency
opportunities
This will not only help bear down on
cost at a time of spiralling energy bills,
it will also enable carbon reduction
strategies and actions linked to
Energy Savings Opportunity Scheme
(ESOS) targets.
DIGITAL SYSTEMS
Machine learning uses artificial
intelligence (AI) to automatically
learn about and improve energy
consumption. It does this by
assimilating half-hourly meter
data and interpreting it in the
context of operations and external
factors (weather, occupancy
levels). This creates ‘fingerprints’
of consumption – and, using AI, the
system then progressively learns
what best performance looks like.
And because the system is smart, it
can learn to ignore outcomes that
are irrelevant, mistaken or due to
bad data.
Crunching data on this scale
manually would require an army of
analysts – but with machine learning,
this can be achieved in quick time,
and lead to a priority action list based
on real world data and comparative
building analysis.
O en, it’s a question of spotting
improvement opportunities hiding
in plain sight, such as equipment
running needlessly or heating
controls incorrectly set - and machine
learning is the perfect tool to do that.
RETAIL SOLUTION
One of Britain’s best-known retail
brands is turning to machine learning
to help optimise energy e iciency
and reduce emissions across its
portfolio of stores.
Central England Co-operative (CEC),
which holds three Carbon Trust
Standards for meeting carbon, waste
and water targets, is now focused on
achieving carbon neutrality by 2030
– and sees AI as a key weapon in its
armoury.
In step with this objective, CEC is
partnering with AMR DNA to optimise
energy performance across its entire
building estate.
This AI-based approach provides
Rob Godson, CEC’s Energy and
Environment Lead, with both
near-real time consumption data
and a smart tool that progressively
optimises energy e iciency in each
building.
“What AMR DNA gives us is a
granular understanding of how our
entire estate is performing from an
energy perspective,” says Godson.
“The so ware automatically overlays
half-hourly metered consumption
data with unusual energy spikes and
their root causes. Issues that would
previously have taken many hours of
manual intervention to analyse can
now be identified automatically – and
quickly resolved.”
CEC adopted the approach having
generated a 206 per cent return on
investment in a pilot project that
identified and eradicated energy
waste. Today, it’s a process helping
CEC to continue its impressive track
record of energy e iciency, having
reduced electricity usage by 51 per
cent and gas consumption by 58 per
cent since 2010.
“Using AMR DNA, we’re able
to monitor energy performance
site by site and benchmark e iciency
and carbon emissions on a like-forlike
basis, whether it’s a convenience
outlet, large store or funeral service.
As our datasets grow, these insights
will become increasingly valuable
in helping us frame the sciencebased
targets that will underpin
our journey to carbon neutrality
and Net Zero.”
SMART ANALYTICS
The so ware also takes account
of changes in store operations,
for example when new freezer
equipment is installed or if a site
moves to 24-hour opening. This
means that only exceptional events
outside the new ‘normal’ are flagged.
It’s also possible to model multiple
building operation scenarios
to inform better consumption
forecasting and strategic planning.
Says Godson: “Our goal as a cooperative
society is carbon neutrality
by 2030, to be achieved through a mix
of energy e iciency actions, waste
reduction and capital investment.
“While there are clearly business
and productivity drivers for
e iciency gains, we also want to be
contributors to the culture shi we
see in our communities towards more
sustainable operations. Thanks to the
granular data now at our disposal,
we’re better placed than ever before
both to bear down on direct carbon
emissions and to shape the sciencebased
targets that will guide CEC to a
carbon neutral future.”
The latest machine learning tools are helping to optimise energy e ciency
and reduce emissions across a retailer’s portfolio of stores. David Sing,
Managing Director (Assets) of Energy Assets
44 JUNE 2022