Where's the value in energy data science? Finding energy savings opportunities in large scale organisational energy data
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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.
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Where's the value in energy data science? Finding energy savings opportunities in large scale organisational energy data
- 1. Where's the value in energy data science? Adrian Friday Professor of Computing & Sustainability
- 2. About me: Energy, CO2e, food, thermal comfort, ICT, life
- 3. Steffen et al. 2015, The Anthropocene Review The Great Acceleration
- 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…
- 10. Energy meters and data loggers
- 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
- 13. What does energy data look like?
- 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…
- 25. Provocations
- 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
- 29. Example from stonybrook.edu, note the ‘tip’
- 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