Paper presented at British Society for Population Studies Conference, 8th September 2014, University of Winchester

1. Developing insight from commercial
data to support #Census2022
BSPS Conference – September 2014
Andy Newing a.newing@soton.ac.uk
Ben Anderson b.anderson@soton.ac.uk (@dataknut)
Sustainable Energy Research Group

2. Census2022: Extracting value from near real time …….. RGS Aug 2014
Overview
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3. Census2022: Extracting value from near real time …….. RGS Aug 2014
What we are trying to do: Census2022
 UK Census 2011/2021 evolution
 Timeliness & cost
 Challenges
 Finding new ways to deliver the Census – ‘Census-like’
 Opportunities
 New kinds of data
 New kinds of social indicators - ‘Census-plus’
 More frequently
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4. Census2022: Extracting value from near real time …….. RGS Aug 2014
Smart metering
• Universal mandate
• Quasi-real time
• High temporal resolution
• Geo-coded
• Reveals actual behaviors
• Near 100% coverage
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5. Census2022: Extracting value from near real time …….. RGS Aug 2014
Smart metering
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Smart
Gas
Meter
Smart
Electricity
Meter
WAN
DCC
Utilities
and
Authorised
Third
Parties
3
In Home
Display(s)
Utility World
managed by the utility
Consumer World
managed by the consumer
2
1
Bills etc.
SMHAN
Smart Meter
Home Area Network
Comms
Hub
Consumer
Gateway(s)
•Appliances
•Consumer HAN
•Internet
•Services
•Future
See also http://www.gov.uk/government/policies/helping-households-to-cut-their-energy-bills/supporting-pages/smart-meters

6. Census2022: Extracting value from near real time …….. RGS Aug 2014
Electricity Load profiles
• Household composition & characteristics
• Ownership and use of appliances
• Habits and routines
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7. Census2022: Extracting value from near real time …….. RGS Aug 2014
Generating area based household
statistics and indicators
Household Load Profiles
Infer household characteristics
Aggregate to small area geographies

8. Census2022: Extracting value from near real time …….. RGS Aug 2014
Smart meter-like dataset

9. Census2022: Extracting value from near real time …….. RGS Aug 2014
UoS Energy Monitoring Study (UoS-E)
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 Smart meter-like household dataset
 n=180
 Repeated surveys:
 characteristics, behaviors and attitudes
 1 second level power import
 Sample: October 2011
 ~ 500m records (1 second)
 Cleaned & checked
 Aggregated (mean power)
 ~ 250,000 records (half hourly)

10. Census2022: Extracting value from near real time …….. RGS Aug 2014
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Descriptive Analysis
1-2 persons vs 3+ Midweek: No children vs 1-2 vs 3+
Midweek: Respondent in
employment vs not

11. Census2022: Extracting value from near real time …….. RGS Aug 2014
Analytically: Load profile indicators
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12. Census2022: Extracting value from near real time …….. RGS Aug 2014
Evening consumption factor (ECF)
Midweek (Tuesday – Thursday)
Ratio of mean 30
minute evening
peak power
import (4pm –
8pm) to off peak
power import
Ψ note: n= 5
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ECF All households Employed Not in active
employment
All households 2.13 1.64
No Children 2.21 2.54 2.09
With Children 2.31 2.29 1.30Ψ

13. Census2022: Extracting value from near real time …….. RGS Aug 2014
Predicting household
characteristics

14. Census2022: Extracting value from near real time …….. RGS Aug 2014
Inferring household characteristics
Presumption of availability
via administrative sources
• Exploratory analysis suggests clear links but
poor explanatory/predictive power
• However … improvements in model fit are
encouraging given limitations of the dataset

15. Census2022: Extracting value from near real time …….. RGS Aug 2014
Inferring household characteristics
using classification
• Classification / cluster
Profile
analysis
Indicators
• Applied within electricity
sector to cluster households
based on their consumption
• Our interest is in underlying
characteristics
• Partitional clustering
technique (k-means)
Consumption
driven clusters
Link to
characteristics
of interest

16. Census2022: Extracting value from near real time …….. RGS Aug 2014

17. Census2022: Extracting value from near real time …….. RGS Aug 2014
Generating area based
statistics

18. Census2022: Extracting value from near real time …….. RGS Aug 2014
In an ideal world…
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This project

19. Census2022: Extracting value from near real time …….. RGS Aug 2014
Realising the ‘added value’ from
domestic smart metering
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Smart
Gas
Meter
Smart
Electricity
Meter
WAN
DCC
Utilities
and
Authorised
Third
Parties
3
In Home
Display(s)
Utility World
managed by the utility
Consumer World
managed by the consumer
2
1
Bills etc.
SMHAN
Smart Meter
Home Area Network
Comms
Hub
Consumer
Gateway(s)
•Appliances
•Consumer HAN
•Internet
•Services
•Future
See also http://www.gov.uk/government/policies/helping-households-to-cut-their-energy-bills/supporting-pages/smart-meters

20. Census2022: Extracting value from near real time …….. RGS Aug 2014
Thank you
 http://www.energy.soton.ac.uk/tag/census2022/
 Ben Anderson b.anderson@soton.ac.uk (@dataknut)
 Andy Newing a.newing@soton.ac.uk
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