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Exploring variability of regular behaviour within households using meter data

Ian Dent, PhD student(psxid@nottingham.ac.uk)Supervisors: Uwe Aickelin, Tom Rodden

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• Massive pressure to reduce carbon usage• Demand must adapt to supply• Demand Side Management one solution

• Interventions to change consumer behaviour

Market Trends

From Tata Power

• For benefit of wider

network

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• To use DSM, need to know existing usage• Standard profiles generated by electricity industry

– (half hour readings)

Usage Profiles

• Differing shapes for

weekday, Sat, Sun

• Peak time of about 4pm to

8pm

• Economy 7 and non E7 only

From Elexon

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• 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 cost and time– However, meter data available for all (in 2020+)

Finding similar households

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• 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

North East Scotland Energy Monitoring Project

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• 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”

Flexibility of household

Approach

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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)

Requirements for better targeting

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• 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

Cluster Validity Indexes

Where intraset distance of

set S consisting of s1 to sN

Where C is set of cluster centres

and Rk are the members of kth cluster

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• Example of one random week (7-11 March 2011), two households, peak period (4pm to 8pm)

• Calculate “minutes after 4pm” – mean and SD

Time of maximum usage

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• 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?

Simple Results

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• 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

Results with extra measures

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• Finding regular activities• Exploring how timing varies

from day to day• Focus on activities and 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

Motifs

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Motif finding using SAX

aabbbbddd

aaabbdddc

Alphabet size (4)

Split points (normal dist)

Motif size (9)

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• 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

Current work

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Finding demographics from meter data

Stereotypes from

Meter data

Demographic

stereotypesCompare

groupings

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• 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

Summary

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• 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 ??

Help please?

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• 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 Pavel Senin of University of Hawaii for code for SAX

References