This document discusses a project called Census2022 that aims to extract value from domestic consumption data from smart meters in a post-census era. It details how smart meter data at high temporal resolution could be aggregated to small geographic areas to generate household statistics and indicators. The document then describes a study conducted with smart meter-like household electricity consumption data from 180 UK homes. Preliminary analysis of load profile indicators showed differences between households of varying sizes and employment statuses. However, more complex models are needed to better predict household characteristics from electricity use alone. Future steps involve accessing larger datasets and creating novel energy-based social indicators.

1. Census2022: Extracting value from
domestic consumption data in a post­census
era
BEHAVE 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 domestic… BEHAVE Sept 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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3. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Smart metering
• Universal mandate
• Geo-coded
• Doesn’t ‘lie’
• (but may be errors/’missing’)
• High temporal resolution
• Near 100% coverage
• Especially for electricity
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Crucial!!

4. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Generating area based household
statistics and indicators
Household Load Profiles
Infer household characteristics
Aggregate to small area geographies

5. Census2022: Extracting value from domestic… BEHAVE Sept 2014
In an ideal world…
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This project

6. Census2022: Extracting value from domestic… BEHAVE Sept 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)

7. Census2022: Extracting value from domestic… BEHAVE Sept 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

8. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Analytically: Load profile indicators
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9. Census2022: Extracting value from domestic… BEHAVE Sept 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

10. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Predicting household characteristics
• Exploratory linear regression
Presumption of
availability via
administrative sources
• clear links but low explanatory power

11. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Conclusions & Next Steps
• Value of pure load profile approaches unclear
• More complex regression models needed
• Exploration of ‘time series’approaches
• Need:
• Access to larger dataset with greater range of household
types
• Creation of ‘census-plus’ indicators
• Novel energy consumption-based social indicators?

12. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Thank you
 http://www.energy.soton.ac.uk/category/research/energy-behaviour/
census-2022/
 Ben Anderson b.anderson@soton.ac.uk (@dataknut)
 Andy Newing a.newing@soton.ac.uk
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