Exploring variability of regular behaviour within households using meter data
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Transcript of Exploring variability of regular behaviour within households using meter data
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Exploring variability of regular behaviour within households using meter data
Ian Dent, PhD student([email protected])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