Exploring variability of regular behaviour within households using meter data

1. Exploring variability of regular behaviour within households using meter data Ian Dent, PhD student (psxid@nottingham.ac.uk) Supervisors: Uwe Aickelin, Tom Rodden

2. Market Trends • Massive pressure to reduce carbon usage • Demand must adapt to supply • Demand Side Management one solution • Interventions to change consumer behaviour • For benefit of wider network From Tata Power

3. Usage Profiles • To use DSM, need to know existing usage • Standard profiles generated by electricity industry • (half hour readings) • Differing shapes for weekday, Sat, Sun • Peak time of about 4pm to 8pm • Economy 7 and non E7 only From Elexon

4. Finding similar households • Cluster similar households on: • Overall shape of usage • Total usage • Other behavioural characteristics • E.g. flexibility of behaviour • Find few (less than 10) stereotypes • Address each differently • Flexible pricing, batteries, external disruption • Cannot collect demographic / attitudinal data for large volumes of households due to costand time • However, meter data available for all (in 2020+)

5. North East Scotland Energy Monitoring Project • 380 households • Year+ of readings • 5 minute sampling • 25 million total readings • Data issues • Missing readings • “wandering” timestamps • “Cleaned” to provide readings exactly on 5 minute boundaries – 288 per day per household • Demographic and attitudinal data also collected • Data courtesy of Tony Craig, The James Hutton Institute, Aberdeen

6. Flexibility of household • Some households are very regimented in their activities • Eat at same times each day • Rise, retire at same times • Others are very variable in their behaviour • Hypothesis: chaotic (very variable) households will accept different behaviour modification interventions than the “creatures of habit” • Many possible measures of variability / “flexibility” • My research is to explore which is “best”

7. Approach

8. Requirements for better targeting Need groupings where each can be represented by a stereotypical user (Courtesy of M. Sarstedt and E. Mooi) • Substantial (large enough to be worth addressing) • Accessible (understandable with observable information) • Actionable (can be addressed) • Stable (remain consistent over time) • Parsimonious (few only) • Familiar(understandable to management) • Relevant (to market of company) • Compact (well separated and internally well connected) • Differentiable (distinguishable conceptually)

9. Cluster Validity Indexes • Hard to pick suitable one • Need to consider all “marketing” aspects • Not just separation and compactness • Need to vary: • number of attributes, differing attributes • Cluster Dispersion Indicator Where C is set of cluster centres and Rk are the members of kth cluster Where intraset distance of set S consisting of s1 to sN

10. Time of maximum usage • Example of one random week (7-11 March 2011), two households, peak period (4pm to 8pm) • Calculate “minutes after 4pm” – mean and SD

11. Simple Results • Kmeans clustering using 2 attributes • Total used • Flexibility (variability of time of maximum usage) • Red – most flexible users • Offer incentives • Black – “stuck in a rut” • Need to address differently – battery?

12. Results with extra measures • Kmeans using 3 attributes • Total electricity • Variability of time of maximum • Variability of time of minimum • Extend to multiple dimensions with other measures of flexibility

13. Motifs • Finding regular activities • Exploring how timing varies from day to day • Focus on activitiesand not individual appliance usage • E.g. cooking evening meal, going to bed, arriving home • Time “stretching”? • Allows for intervention related to particular activity • E.g. free sandwiches

14. Motif finding using SAX aabbbbddd Alphabet size (4) Split points (normal dist) Motif size (9) aaabbdddc

15. Current work • Explore use of differences • Change in use is what is of interest rather than amount of use (i.e. switch something on/off) • Explore parameters • alphabet size • motif size • alphabet assignment (other distributions) • Explore removing repeating characters • Has been useful in other application areas • What is “interesting” – how to automate? • Explore differing collection frequencies

16. Finding demographics from meter data Demographic stereotypes Compare groupings Stereotypes from Meter data

17. Summary • Flexibility concept within load profile analysis • Differing flexibility measures • How to assess “best” • Using motif matching to find regular activities • Usefully addressable clusters • Objective evaluation using cluster validity indices • Validation using demographic and attitudinal data

18. Help please? • Good references I should read • Cluster validity ideas • Ideas for best validity indexes or combination to use • Include some or all of marketing goals • E.g. parsimonious – related to stability measures as numbers of clusters change? • Ideas on how to include in automated evaluation? • Experience of SAX with meter data • Any good ideas ??

19. References • G. Chicco. Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy, 2012. • T. Craig, C. Galan-Diaz, S. Heslop, and J. Polhill. The North East Scotland Energy Monitoring Project (NESEMP). In Workshop on Climate Change and Carbon Management. The James Hutton Institute, March 2012. • DECC. Towards a Smarter Future, Government Response to the Consultation on Electricity and Gas Smart Metering. 2009. • A. Kiprakis, I. Dent, S. Djokic, and S. McLaughlin. Multi-scale Dynamic Modeling to Maximize Demand Side Management. In IEEE Power and Energy Society Innovative Smart Grid Technologies Europe 2011, Manchester, UK, 2011. • C. River. Primer on demand-side management with an emphasis on price-responsive programs. prepared for The World Bank by Charles River Associates, Tech. Rep, 2005. • M. Sarstedt and E. Mooi. A concise guide to market research: The process, data, and methods using IBM SPSS statistics. Springer Verlag, 2011. • J. Shieh and E. Keogh, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008, pp. 623–631. • Thanks to Tony Craig of James Hutton Institute for data. • Thanks to PavelSenin of University of Hawaii for code for SAX