Paper presented at "Spatial Variation in Energy Use, Attitudes and Behaviours: Implications for Smart Grids and Energy Demand", Policy Studies Institute, Friday, 7 February 2014, London, United Kingdom

1. Small Area Estimation as a tool for thinking about
spatial variation in energy demand
Dr Ben Anderson
Sustainable Energy Research Centre
University of Southampton
@dataknut
Spatial Variation in Energy Use, Attitudes and Behaviours:
Implications for Smart Grids and Energy Demand
Policy Studies Institute, London: 7 February 2014

2. Small Area Estimates of Electricity Consumption
Contents
 What & Why
 How?
 Results
– Overall consumption
– Consumption inequalities
 Conclusions & future Directions
@dataknut
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3. Small Area Estimates of Electricity Consumption
Contents
 What & Why
 How?
?
 Results
– Overall consumption
– Consumption inequalities
 Conclusions & future Directions
@dataknut
3

4. Small Area Estimates of Electricity Consumption
Digression: Geography
 Southampton (UK)
@dataknut
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5. Small Area Estimates of Electricity Consumption
Digression: What‟s a small area?
 In this case…
– English Lower Layer
Super Output Areas
– Census 200/2011
LSOAs
– c. 630 households each
– 148 in Southampton City
@dataknut
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6. Small Area Estimates of Electricity Consumption
What & Why
 Basically we want something for nothing
– Small area estimates of energy demand
– Without a bespoke energy census
 Why?
–
–
–
–
@dataknut
Infrastructure planning
Energy efficiency intervention analysis
Energy inequality analysis
Politics!
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7. Small Area Estimates of Electricity Consumption
The problem:
 Small area summaries exist
 But they are aggregates
– Or averages
– Or profiled
 And we want a micro-level simulation model to
assess the socio-economic and spatial impact of
Price changes
Incentive changes
‘Efficiency’ interventions
Changes in ‘energy habits’ (appliances & practices)
Socio-demographic change
@dataknut
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8. Small Area Estimates of Electricity Consumption
What can we do?
 A bespoke energy census
– ££££££££££££££££££££
@dataknut
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9. Small Area Estimates of Electricity Consumption
What can we do?
 A bespoke energy census
– ££££££££££££££££££££
 A large sample energy survey covering all
LSOAs
– ££££££££££
@dataknut
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10. Small Area Estimates of Electricity Consumption
What can we do?
 A bespoke energy census
– ££££££££££££££££££££
 A large sample energy survey covering all LSOAs
– ££££££££££
 Small Area Estimation
– Take existing area level data
– Take (ideally) an existing large n survey
– Combine £
@dataknut
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11. Small Area Estimates of Electricity Consumption
Small Area Estimation
 Econometric approaches
Income, income deprivation,
income inequality
smoking prevalence,
obesity,
consumption expenditure,
CO2, water…
– Well known
– Multi-level Models
– Usually requires census microdata for anything other
Innovation Network:
than means
“Evaluating and improving small area
estimation methods”
 Re-weighting (and other) approaches
– Increasingly http://eprints.ncrm.ac.uk/3210/
well known
– 'Spatial microsimulation'
– Does not require census microdata
@dataknut
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12. Small Area Estimates of Electricity Consumption
Contents
 What & Why
Estimation
 How?
 Results
– Overall consumption
– Consumption inequalities
 Conclusions & Future Directions
@dataknut
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13. Small Area Estimates of Electricity Consumption
Data
 Data
– Living Costs and Food Survey 2008-2010
Consumption proxies (reported energy expenditure)
– Census 2001 (2011)
 Projection/forecasting
– Projected ‘surveys’ -> 2021
– Projected ‘census’ -> 2021
 So far…
@dataknut
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14. Small Area Estimates of Electricity Consumption
Conceptually…
LSOA census ‘constraint’ tables
Survey data cases
If Region = 1
LSOA 1.1
(Region1)
If Region = 2
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Weights
LSOA 2.1
(Region2)
Iterative Proportional Fitting
Ballas et al (2005)
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15. Small Area Estimates of Electricity Consumption
Key First Job:
 Choose your constraints
Census data
Survey data
 You may have little choice
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16. Small Area Estimates of Electricity Consumption
Key First Job:
LSOA census ‘constraint’ tables
 The constraints
– Selected by stepwise regression
Expenditure
Number of persons
Employment Status
Accommodation type
Number of earners
Age of HRP
Age of HRP
Employment Status
Most important
Share of expenditure
Composition
Number of rooms
Number of children
Least important
R sq
@dataknut
Ethnicity (non-white)
0.136
0.01
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17. Small Area Estimates of Electricity Consumption
„Iterative Proportional Fitting‟
 Well known!
 Deming and Stephan 1940
– Fienberg 1970; Wong 1992
 A way of iteratively adjusting statistical tables
– To give known margins (row/column totals)
– ‘Raking’
 In this case
– Create weights for each case so LSOA totals ‘fit’
constraints
– Weighting ‘down’
@dataknut
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18. Small Area Estimates of Electricity Consumption
Internal Validation methods
 Use of constraints to re-create the Census
tables
 Difference = Absolute Error
–
–
Total Absolute Error (TAE) = sum of all
errors
Standardised AE = TAE/(n persons x n
constraint categories)
 Smith et al:
–
SAE of less than 20% and ideally less
than 10%
–
Consumption
Mean SAE
p90
Ethnicity
2.18%
3.05%
Number of children
0.11%
0.22%
Number of rooms
0.05%
0.10%
Employment status
(HRP)
0.88%
1.22%
Age (HRP)
0.34%
0.75%
Tenure
0.07%
0.14%
Accomodation type
0.21%
0.51%
Number of persons
0.00%
0.00%
in 90% of the areas is desirable.
@dataknut
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19. Small Area Estimates of Electricity Consumption
Preliminary results: Electricity
 Mean weekly
household £
 Modelled
 Census 2001
 LC&F Survey 20082010
@dataknut
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20. Small Area Estimates of Electricity Consumption
Validation: Electricity
 Mean weekly
household £
 Observed @LSOA
–
DECC 2010
 Spearman: 0.317
@dataknut
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21. Small Area Estimates of Electricity Consumption
Preliminary results: Electricity
 Total weekly household £
 Modelled
 Census 2001
 LC&F Survey 2008-2010
@dataknut
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22. Small Area Estimates of Electricity Consumption
Validation: Electricity
 Total weekly household £
 Observed @LSOA
–
DECC 2010
 Spearman: 0.509
@dataknut
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23. Small Area Estimates of Electricity Consumption
What is causing the error?
 Heating!
–
@dataknut
2011 data
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24. Small Area Estimates of Electricity Consumption
What is causing the error?
 Housing growth
 Combined:
–
–
@dataknut
Heating = 60%
Growth = 5%
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25. Small Area Estimates of Electricity Consumption
Consumption inequality
 Area level gini
 £ mean spend
 R = -0.413
–
@dataknut
(p < 0.001)
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26. Small Area Estimates of Electricity Consumption
Consumption inequality
 Area level gini
 Index of Multiple
Deprivation 2010
–
Income score
 R = 0.463
–
@dataknut
(p < 0.001)
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27. Small Area Estimates of Electricity Consumption
My big worry
 Data quality
Source: SPRG/ARCC-Water Survey, 2011
www.sprg.ac.uk
@dataknut
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28. Small Area Estimates of Electricity Consumption
Contents
 What & Why
 How?
 Results
– Overall consumption
– Consumption inequalities
 Conclusions & Future Directions
@dataknut
28

29. Small Area Estimates of Electricity Consumption
Conclusions
 Outliers and errors are informative
 Reported consumption data
– Could be dangerous
 Census 2011 central heating
– Critical new constraint
@dataknut
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30. Small Area Estimates of Electricity Consumption
Future directions
 Update for 2011 data
 Census projection 1981 -> 2021
 Use measured energy consumption
– New survey data?
 Contact:
– b.anderson@soton.ac.uk
@dataknut
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