Small Area Estimation as a tool for thinking about temporal and spatial variation in energy demand

Small Area Estimation as a tool for thinking about
temporal and spatial variation in energy demand
Dr Ben Anderson
Sustainable Energy Research Centre
University of Southampton
@dataknut
Aurin Microsimulation Symposium, December 2014
University of Melbourne

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

Contents
 What & Why
 How?
 Results
 Conclusions & future Directions
@dataknut
3
?

Digression: Geography
@dataknut
4
 Southampton (UK)

Digression: What’s a small area?
@dataknut
5
 In this case…
– English Lower Layer
Super Output Areas
– Census 200/2011
LSOAs
– c. 630 households each
– 148 in Southampton City

What & Why
 Basically we want something for nothing
– Small area estimates of energy demand
– Without a bespoke energy census
 Why?
– Infrastructure planning
– Energy efficiency intervention analysis
– Energy inequality analysis
– Politics!
@dataknut
6

What’s the problem?
 Domestic electricity demand is
‘peaky’
 Carbon problems:
 Peak load can demand ‘dirty’
@dataknut
generation
 Cost problems:
 Peak generation is higher priced
energy
 Infrastructure problems:
 Local/national network ‘import’
overload on weekday evenings;
 Local network ‘export’ overload at
mid-day on weekdays due to under-used
PV generation;
 Inefficient use of resources (night-time
trough)
800
700
600
500
400
300
200
100
@dataknut 7
ANZSRAI 2014, Christchurch, New Zealand
UK Housing Energy Fact File
Graph 7a: HES average 24-hour electricity use profile for owner-occupied
homes, England 2010-11
Gas consumption
The amount of gas consumed in the UK varies dramatically between
households. The top 10% of households consume at least four times as
much gas as the bottom 10%.60 Modelling to predict nhouseholds’ e ergy
consumption – based on the property, household income and tenure – has
so far been able to explain less than 40% of this variation.
Gas use varies enormously from
household to household, and the
variation has more to do with
behaviour than how dwellings are
built.
0
00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Heating
Water heating
Electric showers
Washing/drying
Cooking
Lighting
Cold appliances
ICT
Audiovisual
Other
Unknown
Watts
Filling the
trough
Peak load

Data issue:
 Small area summaries exist
 But they are aggregates
– Or averages
– Or estimates
– And they are annual values
 We want a micro-level simulation model to assess the
socio-economic and spatial impact of
@dataknut
 Price changes
 Incentive changes
 ‘Efficiency’ interventions
 Changes in ‘energy habits’ (appliances & practices)
 Socio-demographic change
8

What can we do?
 A bespoke energy census
– ££££££££££££££££££££
@dataknut
9

What can we do?
– ££££££££££££££££££££
 A large sample energy survey covering all
LSOAs
– ££££££££££
@dataknut
10

What can we do?
– ££££££££££££££££££££
 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
11

Small Area Estimation
 Econometric approaches
Income, income deprivation,
– Well known
– Multi-level Models
– Usually requires census microdata for anything other
@dataknut
than means
 Re-weighting (and other) approaches
– Increasingly well known
– 'Spatial microsimulation'
– Does not require census microdata
12
income inequality
smoking prevalence,
obesity,
consumption expenditure,
CO2, water…
Innovation Network:
“Evaluating and improving small area
estimation methods”
http://eprints.ncrm.ac.uk/3210/

Contents
 What & Why
 How?
 Results
 Conclusions & Future Directions
@dataknut
13
Estimation

Data
@dataknut
14
 UK Living Costs and Food Survey 2008-2010
– Consumption proxies (reported energy
expenditure)
 UK Time Use Survey 2001
– Appliance use proxies (modeled energy
demand)
 Census 2001 (2011)

Conceptually…
@dataknut
15
LSOA census ‘constraint’ tables
LSOA 2.1
(Region2)
Survey data cases
LSOA 1.1
(Region1)
Iterative Proportional Fitting
Ballas et al (2005)
If Region = 2
Weights
If Region = 1

Key First Job:
 Choose your constraints
@dataknut
16
Census data
Survey data
 How?
– Selection via regression in micro data
– You may have little choice

‘Iterative Proportional Fitting’
@dataknut
17
 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’

Contents
 What & Why
 How?
 Results
@dataknut
18

Key First Job:
 The constraints
LSOA census ‘constraint’ tables
– Selected by stepwise regression
@dataknut
19
Expenditure Share of expenditure
Most important Number of persons Employment Status
Accommodation type Number of earners
Age of HRP Age of HRP
Employment Status Composition
Number of rooms
Number of children
Least important Ethnicity (non-white)
R sq 0.136 0.01

Internal Validation methods
@dataknut
20
 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%
– in 90% of the areas is desirable.
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%

Preliminary results: Electricity
 Mean weekly
household £
 Modelled
 Census 2001
 LC&F Survey 2008-
2010
@dataknut
21

Validation: Electricity
 Mean weekly
household £
 Observed @LSOA
– DECC 2010
 Spearman: 0.317
@dataknut
22

Preliminary results: Electricity
 Total weekly household £
 Modelled
 Census 2001
 LC&F Survey 2008-2010
@dataknut
23

Validation: Electricity
 Total weekly household £
 Observed @LSOA
– DECC 2010
 Spearman: 0.509
@dataknut
24

What is causing the error?
 Housing growth
Model overestimates
@dataknut
25
Model underestimates

What is causing the error?
 Heating!
– 2011 data
Model overestimates
 Combined:
– Heating = 60%
– Growth = 5%
@dataknut
26
Model underestimates

Consumption inequality
 Area level gini
 £ mean spend
 R = -0.413
– (p < 0.001)
@dataknut
27

Consumption inequality
 Area level gini
 Index of Multiple
Deprivation 2010
– Income score
 R = 0.463
– (p < 0.001)
@dataknut
28

My big worry
 Data quality
@dataknut
29
Source: SPRG/ARCC-Water Survey, 2011
www.sprg.ac.uk

Conclusions
 Outliers and errors are informative
 Reported consumption data
– Could be dangerous
 Census 2011 central heating
– Critical new constraint
@dataknut
30

Contents
 What & Why
 How?
 Results
@dataknut
31

What do we need?
 Model:
– When do people do what at home?
– What energy demand does this generate?
– Scenarios for change
@dataknut
 Appliance efficiency
 Mode of provision
 Changing practices
– What affect might this have for local areas?
32

How might this be done?
 When do people do what at home?
@dataknut
 Time Use Diaries
 What energy demand does this generate?
 Imputed electricity demand for each household
 A microsimulation model of change
 Ideally based on experimental/trial evidence
 Or presumed appliance efficiency gains
 Or ‘what if?’ scenarios of behaviour change
 A way of estimating effects for local areas
 Spatial microsimulation
33
UK ONS 2001
Time Use Survey
J Widén et al., 2009
doi:10.1016/j.enbui
ld.2009.02.013
Using UK Census
2001

When do people do what?
60%
50%
40%
30%
20%
10%
0%
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
@dataknut
60%
50%
40%
30%
20%
10%
Winter (November 2000 - February 2001)
% of respondents reporting a selection of energy-demanding activities
Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) 34
% respondents
Audio
TV
Reading
Computer
Ironing
Laundry
Cleaning
Dish washing
Cooking
Wash/dress self
Aged 25-64 who are in work
0%
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
% respondents
Audio
TV
Reading
Computer
Ironing
Laundry
Cleaning
Dish washing
Cooking
Wash/dress self
Aged 65+

Imputing electricity consumption
90
80
70
60
50
40
30
20
10
@dataknut
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
35
 Imputation at individual level
– For each primary & secondary activity in each 10 minute time
slot
 Then aggregated to household level
– Assume 100W for lighting if at home
– Max: Cooking, Dish Washing, Laundry
– Sum: everything else
 Problems:
– Wash/dress might just be ‘dress’
– Hot water might be gas heated
– TVs might be watched ‘together’
– Not all food preparation = cooking and might be gas
– People have MANY more lights on!
– Several appliances may be ‘on’ but not recorded (Durand-
Daubin, 2013)
– No heating
 => a very simplistic ‘all electricity non-heat’ model!
J Widén et al., 2009
doi:10.1016/j.enbuild.2009.02.013
Assumes ‘shared’ use
Assumes ‘separate’ use
0.00%
0
0:00
1:00
2:00
3:00
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
% of recorded laundry
Mean watts per half hour 'washing/drying'
June ('work days', n = 76) June ('holidays', n = 74) summer laundry (ONS TU Survey 2005)

Results: Mean consumption I
2000
1800
1600
1400
1200
1000
800
600
400
200
@dataknut
36
2000
1800
1600
1400
1200
1000
800
600
400
200
Mean power consumption per half hour in winter (November 2000 - February 2001, all households)
Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) and Model 1
power assumptions
Age of household response person
0
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
Mean consumption per half houir (Watts)
0
1
2
3+
0
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
25-64
65+
Number of earners

Results: Mean consumption II
2000
1800
1600
1400
1200
1000
800
600
400
200
@dataknut
37
0
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
None
One
Two or more
2000
1800
1600
1400
1200
1000
800
600
400
200
0
0:00
2:00
4:00
6:00
8:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
married/partnered
single parent
single person
other
Number of children present Household composition
Mean power consumption per half hour in winter (November 2000 - February 2001, all households)
power assumptions

But this is what the network sees…
1600000
1400000
1200000
1000000
800000
600000
400000
200000
@dataknut
38
1600000
1400000
Morning
1200000
1000000
spike too
800000
600000
spiky!
400000
200000
Sum of power consumption per half hour in winter (November 2000 - February 2001, all households)
power assumptions
Number of earners
0
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
Sum of watts per half hour
3+ earners
2 earners
1 earner
0 earners
0
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
HRP: 75+
HRP: 65-74
HRP: 55-64
HRP: 45-54
HRP: 35-44
HRP: 25-34
HRP: 16-24
Age of household response person

Microsimulation: But what if…?
@dataknut
39
 We change the
washing
assumption?
 => an “all
electricity non-wash,
non-heat’
model!

Sum of power consumption per half hour in winter by number of earners (November 2000 - February 2001, all households)
@dataknut
1600000
1400000
1200000
800000
600000
400000
200000
0
0:00
1:00
2:00
3:00
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
3+
2
1
0
1000000
earners
earners
earner
earners
Now the network sees..
40
power assumptions

But that’s the big picture
@dataknut
41
 We need a way to estimate these totals
– At small area level
 Solution:
– Spatial microsimulation
 IPF re-weighting of survey cases
– Using UK Census 2001
 To match time use survey
– At UK Lower Layer Super Output Area level
 c. 800-900 households
 For Southampton (146 LSOAs)

Key First Job:
 Choose your constraints
@dataknut
42
Census data
Survey data
 How?
– Regression selection methods?
– Whatever is available!

Constraints used
@dataknut
43
 Age of household response person (HRP)
 Ethnicity of HRP
 Number of earners
 Number of children
 Number of persons
 Number of cars/vans
 Household composition (couples/singles)
 Presence of limiting long term illness
 Accommodation type
 Tenure
“Everything”
! Why?
No clear way to
select or
prioritise?

Results (Model 1)
140000
120000
100000
80000
60000
40000
20000
@dataknut
44
140000
120000
100000
80000
60000
40000
20000
Sum of half hourly power consumption (winter 2000/1) by number of earners
Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted), UKL Census
2001 small area tables and Model 1 power assumptions
0
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
3+
2
1
0
0
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
3+
2
1
0
LSOA E01017139: highest % of households with 0
earners in Southampton
LSOA E01017180: lowest % of households with 0

Results (Model 1)
@dataknut
45
Sum of half hourly power consumption (winter 2000/1)
2001 small area tables and Model 1 power assumptions. Map created in R (ggmap)

Results (Model 2)
140000
120000
100000
80000
60000
40000
20000
LSOA E01017139: highest % of households with 0
@dataknut
140000
120000
100000
80000
60000
40000
20000
LSOA E01017180: lowest % of households with 0
46
Sum of half hourly power consumption (winter 2000/1) by number of earners
2001 small area tables and Model 2 power assumptions
0
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
3+
2
1
0
0
0:00
1:30
3:00
4:30
6:00
7:30
9:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
3+
2
1
0

Contents
 What & Why
 How?
 Results
@dataknut
47

Summary & Next Steps
@dataknut
48
 It works!
– A temporal electricity demand spatial microsimulation
– But we don’t know how well
 The model is over-simple
– But we knew that!
 Constraint selection should be evidence based
– ?
 And we need to update to 2011!!
– But no UK time use data since 2005
 Next steps:
– “Solent Achieving Value through Efficiency” (SAVE) project
 Large n RCT tests of demand response interventions
 Linked time use & power monitoring
 (Some) substation monitoring
 => evidence base for model development!
Validation against
observed substation
loads?
Implement more
complex model
(Widen et al, 2010)
or gather better data
Separate ½ hour
models??

Thank you
 b.anderson@soton.ac.uk
 This work has been supported by the UK Low Carbon Network Fund
(LCNF) Tier 2 Programme "Solent Achieving Value from Efficiency
(SAVE)” project:
– http://www.energy.soton.ac.uk/save-solent-achieving-value-from-efficiency/
 STATA code (not the IPF bit):
– https://github.com/dataknut/SAVE
– GPL: V2 - http://choosealicense.com/licenses/gpl-2.0/ applies
@dataknut
@dataknut 49
ANZSRAI 2014, Christchurch, New Zealand

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