Energy Strategies in the retail sector: Linking big data, better data, and people

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1 WORKING with Infrastructure Creation of Knowledge and Energy strategy Development Energy Strategies in the retail sector: Linking big data, better data, and people Dr Katy Janda, Environmental Change Institute, Oxford University WI Co-Investigator and Research Director TDI net Workshop, Nottingham April 27, 2015 April 27, 2015

Dr Katy Janda, Environmental Change Institute, Oxford University WI

2 April 27, 2015

3 The wicked problem of the retail sector UK retail sector is: Key for the UK economy High profile voracious energy consumer Across different scales With distinct access to intel, data,. From data to insights, to knowledge, to strategy, to operations Subject to many paradigm changes Focused on the bottom line February 12, 2015

4 A Better Retailing Climate: Driving Resource Efficiency (Jan 2014) targets to 2020 February 12, 2015

5 Common claims about energy efficiency February 12, 2015

6 DECs February 12, 2015

7 Our perspective on energy management: It isn t easy being green A wicked problem is: Complex and interdependent Difficult to solve (may be difficult to recognize) Addressing one aspect of a wicked problem may reveal (or create) other problems Energy use is a WI sociotechnical problem: The retail sector is diverse and complex. One size will not fit all. Technological solutions must fit organisational characteristics April 27, 2015

may reveal (or create) other problems Energy use is a WI sociotechnical problem: The retail sector is

8 The WI challenge: what new information and tools can help different segments of the retail sector develop proactive energy strategies? Working with Infrastructure Technical Organisational Legal Creation of Knowledge Energy strategy Development April 27, 2015

9 Infrastructure matters Infrastructure Technical Organisational Legal Technical: Many different buildings Old, new, heritage, big, small Lots of different kit Organisational: Different core businesses SMEs, anchor stores, shopping centres, chains, independents Some organisations are data rich AMR and energy managers Others are data poor Legacy meters and little in-house expertis e Legal: Diverse stakeholders Owner-occupiers, tenants, landlords, facilities managers 50% of commercial properties are leased February 12, 2015

Some organisations are data rich AMR and energy managers Others are data poor Legacy meters and little in-house expertis e Legal:

10 Knowledge creation & Energy strategy development are linked Is more data always better? WI will help uncover how much information is needed and by whom, for what purpose. Better energy management is not a one-off. WI will help retail sector stakeholders engage in ongoing strategies to enhance Creation of Knowledge Energy strategy Development competitiveness February 12, 2015

Better energy management is not a one-off.

11 One size solution will not fit all. What about 6 sizes? WI Market segments Leased Space Owner occupiers Landlords Tenants Data Rich AMR + energy managers Type A Type B Type C Data Poor Manual meters, no energy managers Type D Type E Type F April 27, 2015

Tenants Data Rich AMR + energy managers Type A Type B Type C

12 WI solutions Will evolve from interdisciplinary, multi-level collaboration between academia & stakeholders at the intersection of: Top-Down Analytics of the Data Rich Big Data Clever algorithms People & Organizations Fit with management, leases & Staffing Learning from the Middle-Out Better Data Higher resolution ( > 30 min); Easier access to information Bottom-Up: Enriching the Data Poor April 27, 2015

People & Organizations Fit with management, leases & Staffing Learning from the Middle-Out Better

13 Three interlinked workstreams Workstream 1: Top-Down Analytics Peter Grindrod and David Wallom Ramon Granell, RA computing and applied maths Half-hourly energy data Behavioural classification of retail outlet Adaptive rolling forecasts Cross-cutting analysis 100s-1000s of properties All data rich : Types A, B, C Top-Down Analytics of the Data Rich 1 February 12, 2015

classification of retail outlet Adaptive rolling forecasts Cross-cutting analysis 100s-1000s

14 WP1: Top-down learning from the data rich Data set provided by a European retailer <30 min. readings, 663 shops, Apr 2013-Oct 2014> Initial Analysis Consumption analysis using daily profiles Clustering of profiles Temperature relationship with consumption lcluster centroids obtained when clustering shops with the Dirichlet Process Mixture Model algorithm February 12, 2015 Page 14

profiles Clustering of profiles Temperature relationship with consumption lcluster centroids

15 Three interlinked workstreams (2) 2 Workstream 2: Middle-Out Analysis Kathryn Janda and Susan Bright (RA: Julia Patrick) Social science, legal analysis case studies of data rich & poor (Types A-F) Leases & MOUs; property & energy strategies, interviews of personnel at various tiers ~ 50 organisations mix of major retailers and SMEs Learning from the Middle-Out February 12, 2015

(Types A-F) Leases & MOUs; property & energy strategies, interviews of personnel at various

16 Three interlinked workstreams (3) Workstream 3: Bottom-up data collection Malcolm McCulloch and Russell Layberry Ramon Granell, RA engineering and empirical energy analysis Access to premises for data collection; access to personnel for interviews and surveys 6-10 properties (intensive expert analysis) ~ 30 properties (extensive staff engagement) ~100 surveys (mostly data poor : Types D, E, F) 3 Bottom-Up: Enriching the Data Poor February 12, 2015

personnel for interviews and surveys 6-10 properties (intensive expert analysis) ~ 30 properties (extensive

17 Submetering: higher resolution & easier access than a typical smart meter Two methods: Malcolm expert analysis at 1 second intervals Russell To investigate the potential for mobile phones as cheap and powerful remote data loggers Staff participation rather than expert analysis February 12, 2015

investigate the potential for mobile phones as cheap and powerful remote

18 1 phone as an electricity/gas/fluid monitor 2 phone as the communications for a data logger Advantages 1 cheap 2 excellent communications 3 highly configurable February 12, 2015

19 Current partners: In advanced discussion: April 27, 2015

20 WI TAKE AWAYs Best practice depends on how well EE fits Within different organizations Operating in heterogeneous markets With different levels of personnel/staffing & access to data Unlikely that the solution set = 1 Market segmentation WITHIN organizational types may be necessary for policies achieve better results WI thinks the solution set > 1, starting with 6 Socio-technical innovation will drive more change across the sector than technical innovation on its own April 27, 2015

WITHIN organizational types may be necessary for policies achieve better results WI thinks the solution set > 1, starting

21 Publications to date: Janda, K. B., C. Bottrill, & R. Layberry "Learning from the Data Poor: Energy Management in Understudied Organizations " Journal of Property Investment & Finance 32 (4): Janda, K. B., J. Patrick, R. Granell, S. Bright, D. Wallom, & R. Layberry "A WI approach to retail sector energy management." In Proceedings of ECEEE Summer Study, 1-6 June 2015 (Presqu'île de Giens, France). European Council for an Energy-Efficient Economy. Granell, R., C. J. Axon, & D. Wallom "Clustering disaggregated load profiles using a Dirichlet process mixture model." Energy Conversion and Management 92 (March): Bright, S., J. Patrick, B. Thomas, K. B. Janda, E. Bailey, T. Dixon, & S. Wilkinson "The evolution of "greener" leasing practices in Australia and England." In Proceedings of COBRA, July 8-10, 2015 (Sydney, Australia). Royal Institute of Chartered Surveyors (RICS). April 27, 2015

22 Thank You Kathryn Janda April 27, 2015

Energy Management in Small Business - A Review

Energy Management in Small Business - A Review Pre-published version of Janda, K. B., C. Bottrill, & R. Layberry. 2014. "Learning from the Data Poor: Energy Management in Understudied Organizations " Journal of Property Investment & Finance 32 (4),