1. CK ED I W WORKING with Infrastructure Creation of Knowledge and Energy strategy Development http://www.energy.ox.ac.uk/wicked/ 1 Generating Insight from Big Data in Retail Prof David Wallom

2. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED What do you consider Big Data to be? Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy.

3. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED 14 July 2015 Scale matters for problems and solutions in the built environment “stock” at the city, national, international scale The building (or leaseable unit)

4. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED The Challenge  In UK, £1.7 Bn of energy consumed is not managed  Large businesses waste around 15% of energy due to lack of efficiency measures  £5Bn spent on new buildings each year, which use 2-3 times more than designed  Return on investment (ROI) of 48% available for many measures  Effective management will reduce risk and increase security of supply  Market transformation via   Awareness of problem   Understanding of technical performance   Opportunities for socio- technical change 14 July 2015 Source: Sweett Group, Verco, UCL, & Energy Foundation. 2014. Operational Energy Use and the Use of 'Bigger, Better Data". GCB230. Green Construction Board: London The Opportunity

5. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED What can analytics say about energy usage?  Turning Data into Actionable Information;  Predicting and classifying costs when there is a shift in the type of tariff, e.g. shifting to a real-time tariff from a fixed price tariff.  Clustering of load profiles, determining behaviour type and response by the consumer  Determining fundamental drivers of energy consumption

6. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED Clustering load profiles using Bayesian analytic techniques

7. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED

8. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED Normalised daily power demand profiles for all businesses by sector Commercial energy consumption and real time pricing  Analyse the impact of introduction of time-of-use and real- time pricing strategies

9. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED

10. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED How is big data best collected and stored, and who ought to have access?  Data costs, to both move and store, BIG DATA costs A LOT!  Maximise value of data once you have it by making it uniformly accessible to business functions  Increasingly common that energy information is handled on behalf of the retailer by a third party contractor.  Lack of standards in all aspects of data formats  Changing energy management provider is an epic task.  Example, we have partnerships within WICKED with four different retailers, four different configurations of data and inconsistent metadata.

11. W I CK ED http://www.energy.ox.ac.uk/wicked/ W I CK ED  Retail uses analytics regularly in sales, now it can move to other parts of the business,  Answer fundamental questions on efficacy of operational changes w.r.t energy  Connect consumption to overall business performance.