The document outlines the goals of the Census2022 project, focusing on innovative methods to utilize near real-time data, particularly from smart metering, to enhance UK census practices. It discusses the potential of smart metering data to infer household characteristics and generate more timely and cost-effective social indicators. The project aims to leverage this data to create valuable insights into household behaviors and demographics, thereby improving census accuracy and relevance.

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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