A new arsenal of artificial intelligence and data science tools will unlock massive energy savings and help UK business in their goal of achieving net zero. These cutting-edge algorithms will automatically and continuously sift through a deluge of data and find new insights and recommend ways to slash energy consumption. The Net0Insights project (http://net0i.org) in partnership with industry is working towards this objective. In this talk we will reflect on the challenges of opportunities of making sense of organisations through their energy data footprint. We both identify how these data can be a valuable resource and what organisations need to do to yield more value from it, but also, question whether this very data science/IoT/digital twin approach is a sufficiently large piece of the puzzle of addressing net zero.
3. Steffen et al. 2015, The Anthropocene Review
The Great Acceleration
4.
5. Net0Insights project: “replicable energy
savings”
• These cutting-edge algorithms will automatically and
continuously sift through a deluge of data and find new insights
and recommend ways to slash energy consumption.
“increasingly intelligent energy management
is required to address the societal energy and
climate issues we face”
Grant ref: EP/T025964/1
6. 3 points to make
ICT may help us find
energy savings from
existing energy data
1
Important missing
context data is not
routinely collected
2
Systemic drivers and
understanding
needed to drive
reductions
3
7. Our project starting point, 3 assertions
Energy and building
management system
data from industrial
partners exists
Time-series and
statistical methods
(e.g. changepoints)
yields insights
Help stakeholders
focus on where to
make savings/ lower
cost of analysis
8. 1,000s of channels, 1,000,000s of data points
“They hunted till darkness came on, but they found
Not a button, or feather, or mark,
By which they could tell that they stood on the ground
Where the Baker had met with the Snark.”
The Hunting of the Snark, Lewis Carroll, 1876.
9. Want to tell you a story…
• Of gathering lots of energy data from our partners
• Working with them to understand what it means to them
• Trying to render it useful for ‘energy insight generation’ using largely
statistical methods and HCI
• And why this is hard in practice…
11. Physical/digital, energy and purpose
intertwined
Note peaks and base load. Energy often exhibits seasonality
driven by organisational practice
12. 3 immediately useful things for energy
managers
Identifying ‘broken’ sensors/ data pipeline
Reducing avoidable energy waste (classic: lights left on, overheating)
Normative performance comparisons (my lecture theatre compared
with a well performing one)
These are all types of anomaly we can detect
14. Going to be looking at energy data traces a
fair bit…
This Photo by Unknown Author is licensed under CC BY-NC
06:00 12:00 18:00 00:00
1000
2000
3000
4000
5000
Timestamp
Electric
power
(Watts)
Lighting
Refrigeration
Entertainment & IT
Other cooking appliances
Oven
Time
Power
15. How often is important, energy
infrastructures get ‘woven into’ life
75W x 1 hour, 3kW x ~4 mins, 3kW x 20 hours Energy is an enabling infrastructure that
changes society… ‘path dependence’
16. What is ‘a changepoint’ anyway?
• A family of statistical methods to
partitioning time series data
where the statistical properties
change (e.g. mean, SD)
• But how many are there? And
can we make it work with ‘real
world’ energy data?
17. Going to look at four exemplars
To illustrate some key points about sense making and understanding energy data in
‘real world’ contexts
18. Case study 1 – preschool centre historic
electricity, anomaly is energy waste?
None of the stakeholders were able to explain this, and the preschool
centre itself were naturally busy doing childcare!
19. Case study 2 – Fryers in quick service
restaurants, anomaly or working practice?
Energy use is linked to sales and ‘normal operation’, but the data from
the two isn’t integrated – staff churn frustrates savings measures
20. Case study 3 – heating a complex office,
anomaly or poor infrastructure?
Heat
gains
from ‘use’
Heat to
‘achieve
comfort’
21. Case study 3 – note building systems thinks
there’s no problem!
Gap between
infrastructure and
experience (blue
= BMS, yellow =
South)
i.e. we should question whether the data we have is ‘the lived
experience’
22. Case study 4 – zooming out, anomalous
behaviour or business as usual?
Why wasn’t this lower during COVID? Why has
it rebounded in some cases?
23. In short: be extremely
suspicious of quick AI/IoT
techno-fixes!
How do we enable systemic infrastructure change and actually
transform to sustainable living?
24. Existing infrastructures embed history and
practice
Existing infrastructures may be limited (not granular/ uniformly granular, especially in older buildings)
Topologically complex and interrelated, but often unclear how
The result of years of evolution, outsourcing and organisational change
Little business case to install substantial new monitoring
Ownership, trust, fear increasingly of GDPR, cyber attack… that’s even if it’s being gathered
systematically or they own it…
26. Digital technology is making
smarter environments
Can it really be an engine for sustainability? Or are we just adding more
tech(nofixes)? Addressing climate change is more than an engineering
problem!
27. We’ve found
• Little data maturity (little end-to-end data
quality, budget, even outsourcing)
• Lack of institutional recording of important
context
• Silo-ing of sustainability with (overworked)
energy managers/ consultants
• Limited or missing pressure on organisations to
report and disclose their performance
28. Energy is the business,
but not core business
• Energy underpins everyday life
(certainly in modernist 1st world
context)
• We can push to optimise what we do
• But ultimately what we do needs to be
held up and considered in the broader
context of the organisation
31. So, premise is true?
• The ‘data deluge’ exists, lack of
organisational capacity to analyse all
this!
• Insights, anomalies and features can be
found in such data
• But, differentiating from actual
business requires significant new
context that is often lost
• Organisations lack the processes and
infrastructures to capture this at all
levels
32. What is the role of energy data
science?
• Energy insights do exist!
• But are they more valuable in what they tell
us about business as usual not user
behaviour change?
• But…
• Shouldn’t we be focusing on the larger
drivers of energy use?
• Energy enables ‘business as usual’ and this
and its system impacts that need to be
questioned
33. Socio-Digital Sustainability Team
Kathy New
PhD Student
Dr Kelly Widdicks
User research, CEH
Adam Tyler
PhD Student
Dr Oliver Bates
Senior Researcher
Dr Ally Gormally-Sutton
Senior Lecturer
Prof Adrian Friday
Professor
Dr Janine Morley
Researcher
http://www.lancaster.ac.uk/scc-sds/
Matt Marsden
PhD Student
Christina Bremer
PhD Student
Iman Hussain
PhD Student
Dr Christian Remy
Senior Researcher
Dr Carolynne Lord
Senior Researcher