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Electricity consumption and household
characteristics: Implications for census-
taking in a smart metered future
Ben Anderson (b.anderson@soton.ac.uk, @dataknut)
Sharon Lin (X.Lin@soton.ac.uk)
Engineering & Environment (Energy & Climate Change)
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
The menu
 Background
 Smart meter electricity data
 Example:
– Inferring household attributes
 Implications
 Where next?
2
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Background
• Timeliness & cost
• Indicators
UK Census evolution
• Finding new ways to deliver the Census
Challenges
Opportunities
3
Owen. 2006. The rise of the
machines—a review of energy using
products in the home from the 1970s to
today, Energy Saving Trust, London.
flickr.com/photos/82655797@N00/8249565455
2010s
pixabay
Old indicators –
Census-like?
New indicators –
Census-plus?
Higher frequency? New users/markets?
New data
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Distribution of Gas Central Heating, Census 2011
Source: http://datashine.org.uk
Special interest: Electricity
• Unlike gas (c. 90%)
Near universal availability
• Unlike gas (c. 85%)
Near universal uptake
• Unlike water (c. 45%)
100% metered
4
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Distribution of Gas Central Heating, Census 2011
Source: http://datashine.org.uk
Special interest: Electricity
• Unlike gas (c. 90%)
Near universal availability
• Unlike gas (c. 85%)
Near universal uptake
• Unlike water (c. 45%)
100% metered
5
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Inspiration I: Census-like
6
Census 2011 household level variables* Existing evidence for links to load profiles
Household Number of persons (Beckel et al., 2013)
Presence of person with limiting long term illness
Number of children (Yohanis et al., 2008)
Age distributions of all persons
Dwelling Household dwelling type (Firth et al., 2008; McLoughlin et al., 2012)
Household tenure (Druckman and Jackson, 2008)
Number of (bed)rooms dwelling floor area as a proxy
Number of cars/vans
Presence of and fuel used for heating (McLoughlin et al., 2013)
Householder Ethnic group/country of birth of HRP/main language
Age of HRP (McLoughlin et al., 2013)
NS-SEC of household reference person (HRP) (Druckman and Jackson, 2008; Hughes and
Moreno, 2013; McLoughlin et al., 2013)
Economic activity of HRP/hours worked (Yohanis et al., 2008; McLoughlin et al., 2013)
HRP Education level
Marital Status
* Taken from ONS. 2014a. 2011 Census User Guide - 2011 Census Variable and Classification Information: Part 3. Newport: Office for National
Statistics.
Using ‘profile
indicators’
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Inspiration II: Census-plus
7
Census-plus indicators Existing evidence for links to load profiles
Other Dwelling floor area (Beckel et al., 2013; Craig et al., 2014; McLoughlin et
al., 2013)
Household Income (Beckel et al., 2013; Craig et al., 2014; McLoughlin et
al., 2013)
Consumption profile segments Haben et al (2013)
Indicators of routine ?
Using ‘profile
indicators’
Source: Newing, Andy, Ben Anderson, AbuBakr Bahaj, and Patrick James. 2015. ‘The Role of Digital Trace Data in
Supporting the Collection of Population Statistics - the Case for Smart Metered Electricity Consumption Data’.
Population, Space and Place, July, doi:10.1002/psp.1972.
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Inspiration III: Data
8
Data: UoS Small Scale
Smart Meter Trial (n =
95)
Source: Newing, Andy, Ben Anderson, AbuBakr Bahaj, and Patrick James. 2015. ‘The Role of Digital Trace Data in
Supporting the Collection of Population Statistics - the Case for Smart Metered Electricity Consumption Data’.
Population, Space and Place, July, doi:10.1002/psp.1972.
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Inspiration IV: Data
9
Weekdays Weekends
Source: Irish CER Smart
Meter Trial data October
2009 ( n = 3,488)
www.ucd.ie/issda/data/com
missionforenergyregulation
cer/
0
0.2
0.4
0.6
0.8
1
1.2
12:00:00AM
1:30:00AM
3:00:00AM
4:30:00AM
6:00:00AM
7:30:00AM
9:00:00AM
10:30:00AM
12:00:00PM
1:30:00PM
3:00:00PM
4:30:00PM
6:00:00PM
7:30:00PM
9:00:00PM
10:30:00PM
Meankwh(October2009)
0
0.2
0.4
0.6
0.8
1
1.2
12:00:00AM
1:30:00AM
3:00:00AM
4:30:00AM
6:00:00AM
7:30:00AM
9:00:00AM
10:30:00AM
12:00:00PM
1:30:00PM
3:00:00PM
4:30:00PM
6:00:00PM
7:30:00PM
9:00:00PM
10:30:00PM
Meankwh(October2009)
In work
Caring for
family/relative
Unemployed
Retired
HRP work status
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Inspiration IV: Data
10
Source: Irish CER Smart
Meter Trial data October
2009 ( n = 3,488)
www.ucd.ie/issda/data/com
missionforenergyregulation
cer/
Weekend
HRP work status
Week 1, October 2009
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Research Pathway
Sample
data
• ‘Labelled’
consumption data
• Models
Sample of
small
areas
• ‘Unlabelled’ consumption
• Geo-coded
• ~100% coverage
Validate
models
• Using Census
2011 LSOA/OA
data
11
This project
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
The menu
 Background
 Smart meter electricity data
 Example:
– Inferring household attributes
 Implications
 Where next?
12
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
‘Smart Meter’ data options
• UoS Energy Study (n = ~180)
• Irish CER Smart Meter Trial (n = 4,000)
• Energy Demand Reduction Project (n=
14,000)
‘Labelled’ consumption data
• ?
‘Unlabelled’ but geocoded data
13
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
‘Smart Meter’ data options
• UoS Energy Study (n ~= 180)
• Irish CER Smart Meter Trial (n ~= 4,000)
‘Labelled’ data
• Energy Demand Reduction Project (n ~= 14,000)
‘Unlabelled’ and non-geocoded
• ?
‘Unlabelled’ but geocoded data
14
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
CER Irish Smart Meter Trials
15
Sample
Method
unclear
N = ~
4,000
Geography
unknown
Study
groups
Trial &
control
Household
Survey
Pre trial (2009) Post trial
(2010)
Electricity
kWh per half
hour for
24months
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Processing & Cleaning
16
Analytic sample
October 2009 – 5.6 million ½ hour records
October
2009
Outliers
Non-
domestic
157 million ½ hour records
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
The menu
 Background
 Smart meter electricity data
 Example:
– Inferring household attributes
 Implications
 Where next?
17
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Steps:
• Select potential power consumption (profile) indicators
Step 1
• Test if household characteristics can predict indicators
Step 2
• Estimate household characteristics from indicators
Step 3 (reverse step 2)
18
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
0
0.2
0.4
0.6
0.8
1
1.2
Meankwh(October2009)
Step 1: Profile indicators
19
Simplification
&
Experimentation
Peak:
•Magnitude
•Timing of peak
Mean baseload
consumption
Overall mean
consumption
Daily sum
Daily 97.5th
percentile
Ratio of
evening peak
mean to non-
evening peak
mean (ECF)
Ratio of daily
mean to peak
(Load Factor)
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Characteristics of interest
• Income
• Floor area
• Employment status
Census-like:
• Number of residents
• Presence of children
Given that we know:
20
DWP, HMRC,
NHS etc
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Step 2: Profile indicators prediction
• October 2009
Mixed effects model
• 3 * 4 =12 repeat observations
Mid-week (Tues – Thurs)
• 2 * 4 = 8 repeated observations
Weekend (Sat & Sun)
21
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Step 2: Profile indicators prediction
22
Profile indicators (LHS) Predictors (RHS)
Daily Peak time Income
Daily peak demand 06:00 to 10.30
Floor area
Daily average baseload demand (02:00 -
05:00)
Employed (response person)
Daily mean consumption
Retired (response person)
Daily sum of consumption
[Number of residents]
Daily 97.5th percentile consumption
[Presence of children]
Evening Consumption Factor
Load factor
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Results: Weekend
23
Dailypeak
time
Dailypeak
06:00to
10.30
Daily
average
baseload
(02:00-
05:00)
Daily
average
Dailysum
Daily97.5th
percentile
Evening
Consumptio
nFactor
Loadfactor
Predictors
Number of residents - ✔ ✔ ✔
Presence of children - ✔ ✔ ✔ ✔
Income
Not when
employme
nt & floor
area
included - ✔
Not when
employme
nt included
Not when
employme
nt included
Not when
employment
& floor area
included - -
Floor area - - ✔ - - - - ✔
Employment - - - - - - - -
Retired - - - - - - - -
Residual 78% 35% 40% 21% 21% 34% 65% 50%
Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488)
www.ucd.ie/issda/data/commissionforenergyregulationcer/
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Results: Midweek
24
Daily
peak
time
Daily
peak
06:00to
10.30
Daily
average
baseload
(02:00-
05:00)
Daily
average
Daily
sum
Daily
97.5th
percentil
e
Evening
Consum
ption
Factor
Load
factor
Predictors
Number of residents - ✔
Not when
employme
nt & floor
area
included ✔ ✔ ✔
Presence of children
Not when
employme
nt & floor
area
included ✔ ✔ ✔ ✔
Not when
employme
nt & floor
area
included
Income -
Not when
employme
nt included ✔
Not when
employme
nt & floor
area
included
Not when
employme
nt & floor
area
included
Not when
employme
nt & floor
area
included - -
Floor area - - - - - - - -
Employment - - - - - - ✔ ✔
Retired - - - - - - - -
Residual 80% 37% 46% 23% 23% 36% 72% 51%
Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488)
www.ucd.ie/issda/data/commissionforenergyregulationcer/
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Step 3: Can we predict attributes?
• HRP in Employment
Logit model
• Income
• Floor area
Linear model
• Assume we know n people & n children
As before
25
DWP,
HMRC,
NHS etc
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Employment Status Model I
 Midweek:
26
 Correct classification
– Without ‘energy’ = 65.01%
Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488)
www.ucd.ie/issda/data/commissionforenergyregulationcer/
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Employment Status Model I
 Midweek:
27
 Correct classification
– Without ‘energy’ = 65.01%
– With ‘energy’ = 65.42% !
Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488)
www.ucd.ie/issda/data/commissionforenergyregulationcer/
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
1. Profile indicators
2. Profile cluster membership (new)
3. Indicator of habit (new)
4. ‘Admin’ data
Employment Status Model II
28
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
MeankWh(weekdays)
1
2
3
4
5
6
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Employment Status Model II
29Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488)
www.ucd.ie/issda/data/commissionforenergyregulationcer/
‘Habit: Tomorrow’
‘Habit: Day after tomorrow’
‘HRP in employment’
‘Admin’ data
Profile indicators
Profile cluster membership
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Implications?
• ‘Energy’ may not help much
• But it could help to validate
If we have admin data
• Profile indicators may have
value
But if we don’t…
30
For the
variables
tested…
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Where next?
Sample
data
• ‘Labelled’
consumption data
• Models
Sample of
small
areas
• ‘Unlabelled’ consumption
• Geo-coded
• ~100% coverage
Validate
models
• Using Census
2011 LSOA/OA
data
31
This project
We need
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
“practices leaves all sorts of “marks” – diet shows up
in statistics on obesity; heating and cooling practices
have effect on energy demand, and habits of laundry
matter for water consumption. Identifying relevant
“proxies” represents one way to go.”
Sustainable Practices Research Group Discussion Paper
www.sprg.ac.uk
Image: Eric Shipton
The Future: Practice hunting?
32
Owen. 2006. The rise of the
machines—a review of energy
using products in the home from
the 1970s to today, Energy
Saving Trust, London.
flickr.com/photos/82655797@N00/8249565455
2010s
pixabay
Transformative Research Programme: #Census2022
www.energy.soton.ac.uk/tag/census2022/
Thank you
 Contact:
– b.anderson@soton.ac.uk
– @dataknut
 Project:
– www.energy.soton.ac.uk/tag/census2022/
 Selected code:
– github.com/dataknut/Census2022
 Papers to date:
– Claxton, R, J Reades, and B Anderson. (2012). ‘On the Value of Digital Traces for Commercial Strategy
and Public Policy: Telecommunications Data as a Case Study’. In The Global Information Technology
Report 2012, edited by S Dutta and B Bilbao-Osorio. Geneva: World Economic Forum.
– Newing et al (2015) ‘The Role of Digital Trace Data in Supporting the Collection of Population Statistics
- the Case for Smart Metered Electricity Consumption Data’. Population, Space and Place, July,
doi:10.1002/psp.1972.
33

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Electricity consumption and household characteristics: Implications for census-taking in a smart metered future

  • 1. Electricity consumption and household characteristics: Implications for census- taking in a smart metered future Ben Anderson (b.anderson@soton.ac.uk, @dataknut) Sharon Lin (X.Lin@soton.ac.uk) Engineering & Environment (Energy & Climate Change)
  • 2. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ The menu  Background  Smart meter electricity data  Example: – Inferring household attributes  Implications  Where next? 2
  • 3. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Background • Timeliness & cost • Indicators UK Census evolution • Finding new ways to deliver the Census Challenges Opportunities 3 Owen. 2006. The rise of the machines—a review of energy using products in the home from the 1970s to today, Energy Saving Trust, London. flickr.com/photos/82655797@N00/8249565455 2010s pixabay Old indicators – Census-like? New indicators – Census-plus? Higher frequency? New users/markets? New data
  • 4. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Distribution of Gas Central Heating, Census 2011 Source: http://datashine.org.uk Special interest: Electricity • Unlike gas (c. 90%) Near universal availability • Unlike gas (c. 85%) Near universal uptake • Unlike water (c. 45%) 100% metered 4
  • 5. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Distribution of Gas Central Heating, Census 2011 Source: http://datashine.org.uk Special interest: Electricity • Unlike gas (c. 90%) Near universal availability • Unlike gas (c. 85%) Near universal uptake • Unlike water (c. 45%) 100% metered 5
  • 6. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Inspiration I: Census-like 6 Census 2011 household level variables* Existing evidence for links to load profiles Household Number of persons (Beckel et al., 2013) Presence of person with limiting long term illness Number of children (Yohanis et al., 2008) Age distributions of all persons Dwelling Household dwelling type (Firth et al., 2008; McLoughlin et al., 2012) Household tenure (Druckman and Jackson, 2008) Number of (bed)rooms dwelling floor area as a proxy Number of cars/vans Presence of and fuel used for heating (McLoughlin et al., 2013) Householder Ethnic group/country of birth of HRP/main language Age of HRP (McLoughlin et al., 2013) NS-SEC of household reference person (HRP) (Druckman and Jackson, 2008; Hughes and Moreno, 2013; McLoughlin et al., 2013) Economic activity of HRP/hours worked (Yohanis et al., 2008; McLoughlin et al., 2013) HRP Education level Marital Status * Taken from ONS. 2014a. 2011 Census User Guide - 2011 Census Variable and Classification Information: Part 3. Newport: Office for National Statistics. Using ‘profile indicators’
  • 7. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Inspiration II: Census-plus 7 Census-plus indicators Existing evidence for links to load profiles Other Dwelling floor area (Beckel et al., 2013; Craig et al., 2014; McLoughlin et al., 2013) Household Income (Beckel et al., 2013; Craig et al., 2014; McLoughlin et al., 2013) Consumption profile segments Haben et al (2013) Indicators of routine ? Using ‘profile indicators’ Source: Newing, Andy, Ben Anderson, AbuBakr Bahaj, and Patrick James. 2015. ‘The Role of Digital Trace Data in Supporting the Collection of Population Statistics - the Case for Smart Metered Electricity Consumption Data’. Population, Space and Place, July, doi:10.1002/psp.1972.
  • 8. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Inspiration III: Data 8 Data: UoS Small Scale Smart Meter Trial (n = 95) Source: Newing, Andy, Ben Anderson, AbuBakr Bahaj, and Patrick James. 2015. ‘The Role of Digital Trace Data in Supporting the Collection of Population Statistics - the Case for Smart Metered Electricity Consumption Data’. Population, Space and Place, July, doi:10.1002/psp.1972.
  • 9. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Inspiration IV: Data 9 Weekdays Weekends Source: Irish CER Smart Meter Trial data October 2009 ( n = 3,488) www.ucd.ie/issda/data/com missionforenergyregulation cer/ 0 0.2 0.4 0.6 0.8 1 1.2 12:00:00AM 1:30:00AM 3:00:00AM 4:30:00AM 6:00:00AM 7:30:00AM 9:00:00AM 10:30:00AM 12:00:00PM 1:30:00PM 3:00:00PM 4:30:00PM 6:00:00PM 7:30:00PM 9:00:00PM 10:30:00PM Meankwh(October2009) 0 0.2 0.4 0.6 0.8 1 1.2 12:00:00AM 1:30:00AM 3:00:00AM 4:30:00AM 6:00:00AM 7:30:00AM 9:00:00AM 10:30:00AM 12:00:00PM 1:30:00PM 3:00:00PM 4:30:00PM 6:00:00PM 7:30:00PM 9:00:00PM 10:30:00PM Meankwh(October2009) In work Caring for family/relative Unemployed Retired HRP work status
  • 10. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Inspiration IV: Data 10 Source: Irish CER Smart Meter Trial data October 2009 ( n = 3,488) www.ucd.ie/issda/data/com missionforenergyregulation cer/ Weekend HRP work status Week 1, October 2009
  • 11. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Research Pathway Sample data • ‘Labelled’ consumption data • Models Sample of small areas • ‘Unlabelled’ consumption • Geo-coded • ~100% coverage Validate models • Using Census 2011 LSOA/OA data 11 This project
  • 12. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ The menu  Background  Smart meter electricity data  Example: – Inferring household attributes  Implications  Where next? 12
  • 13. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ ‘Smart Meter’ data options • UoS Energy Study (n = ~180) • Irish CER Smart Meter Trial (n = 4,000) • Energy Demand Reduction Project (n= 14,000) ‘Labelled’ consumption data • ? ‘Unlabelled’ but geocoded data 13
  • 14. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ ‘Smart Meter’ data options • UoS Energy Study (n ~= 180) • Irish CER Smart Meter Trial (n ~= 4,000) ‘Labelled’ data • Energy Demand Reduction Project (n ~= 14,000) ‘Unlabelled’ and non-geocoded • ? ‘Unlabelled’ but geocoded data 14
  • 15. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ CER Irish Smart Meter Trials 15 Sample Method unclear N = ~ 4,000 Geography unknown Study groups Trial & control Household Survey Pre trial (2009) Post trial (2010) Electricity kWh per half hour for 24months
  • 16. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Processing & Cleaning 16 Analytic sample October 2009 – 5.6 million ½ hour records October 2009 Outliers Non- domestic 157 million ½ hour records
  • 17. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ The menu  Background  Smart meter electricity data  Example: – Inferring household attributes  Implications  Where next? 17
  • 18. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Steps: • Select potential power consumption (profile) indicators Step 1 • Test if household characteristics can predict indicators Step 2 • Estimate household characteristics from indicators Step 3 (reverse step 2) 18
  • 19. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ 0 0.2 0.4 0.6 0.8 1 1.2 Meankwh(October2009) Step 1: Profile indicators 19 Simplification & Experimentation Peak: •Magnitude •Timing of peak Mean baseload consumption Overall mean consumption Daily sum Daily 97.5th percentile Ratio of evening peak mean to non- evening peak mean (ECF) Ratio of daily mean to peak (Load Factor)
  • 20. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Characteristics of interest • Income • Floor area • Employment status Census-like: • Number of residents • Presence of children Given that we know: 20 DWP, HMRC, NHS etc
  • 21. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Step 2: Profile indicators prediction • October 2009 Mixed effects model • 3 * 4 =12 repeat observations Mid-week (Tues – Thurs) • 2 * 4 = 8 repeated observations Weekend (Sat & Sun) 21
  • 22. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Step 2: Profile indicators prediction 22 Profile indicators (LHS) Predictors (RHS) Daily Peak time Income Daily peak demand 06:00 to 10.30 Floor area Daily average baseload demand (02:00 - 05:00) Employed (response person) Daily mean consumption Retired (response person) Daily sum of consumption [Number of residents] Daily 97.5th percentile consumption [Presence of children] Evening Consumption Factor Load factor
  • 23. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Results: Weekend 23 Dailypeak time Dailypeak 06:00to 10.30 Daily average baseload (02:00- 05:00) Daily average Dailysum Daily97.5th percentile Evening Consumptio nFactor Loadfactor Predictors Number of residents - ✔ ✔ ✔ Presence of children - ✔ ✔ ✔ ✔ Income Not when employme nt & floor area included - ✔ Not when employme nt included Not when employme nt included Not when employment & floor area included - - Floor area - - ✔ - - - - ✔ Employment - - - - - - - - Retired - - - - - - - - Residual 78% 35% 40% 21% 21% 34% 65% 50% Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488) www.ucd.ie/issda/data/commissionforenergyregulationcer/
  • 24. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Results: Midweek 24 Daily peak time Daily peak 06:00to 10.30 Daily average baseload (02:00- 05:00) Daily average Daily sum Daily 97.5th percentil e Evening Consum ption Factor Load factor Predictors Number of residents - ✔ Not when employme nt & floor area included ✔ ✔ ✔ Presence of children Not when employme nt & floor area included ✔ ✔ ✔ ✔ Not when employme nt & floor area included Income - Not when employme nt included ✔ Not when employme nt & floor area included Not when employme nt & floor area included Not when employme nt & floor area included - - Floor area - - - - - - - - Employment - - - - - - ✔ ✔ Retired - - - - - - - - Residual 80% 37% 46% 23% 23% 36% 72% 51% Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488) www.ucd.ie/issda/data/commissionforenergyregulationcer/
  • 25. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Step 3: Can we predict attributes? • HRP in Employment Logit model • Income • Floor area Linear model • Assume we know n people & n children As before 25 DWP, HMRC, NHS etc
  • 26. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Employment Status Model I  Midweek: 26  Correct classification – Without ‘energy’ = 65.01% Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488) www.ucd.ie/issda/data/commissionforenergyregulationcer/
  • 27. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Employment Status Model I  Midweek: 27  Correct classification – Without ‘energy’ = 65.01% – With ‘energy’ = 65.42% ! Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488) www.ucd.ie/issda/data/commissionforenergyregulationcer/
  • 28. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ 1. Profile indicators 2. Profile cluster membership (new) 3. Indicator of habit (new) 4. ‘Admin’ data Employment Status Model II 28 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 MeankWh(weekdays) 1 2 3 4 5 6
  • 29. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Employment Status Model II 29Data: Irish CER Smart Meter Trial data October 2009 ( n = 3,488) www.ucd.ie/issda/data/commissionforenergyregulationcer/ ‘Habit: Tomorrow’ ‘Habit: Day after tomorrow’ ‘HRP in employment’ ‘Admin’ data Profile indicators Profile cluster membership
  • 30. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Implications? • ‘Energy’ may not help much • But it could help to validate If we have admin data • Profile indicators may have value But if we don’t… 30 For the variables tested…
  • 31. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Where next? Sample data • ‘Labelled’ consumption data • Models Sample of small areas • ‘Unlabelled’ consumption • Geo-coded • ~100% coverage Validate models • Using Census 2011 LSOA/OA data 31 This project We need
  • 32. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ “practices leaves all sorts of “marks” – diet shows up in statistics on obesity; heating and cooling practices have effect on energy demand, and habits of laundry matter for water consumption. Identifying relevant “proxies” represents one way to go.” Sustainable Practices Research Group Discussion Paper www.sprg.ac.uk Image: Eric Shipton The Future: Practice hunting? 32 Owen. 2006. The rise of the machines—a review of energy using products in the home from the 1970s to today, Energy Saving Trust, London. flickr.com/photos/82655797@N00/8249565455 2010s pixabay
  • 33. Transformative Research Programme: #Census2022 www.energy.soton.ac.uk/tag/census2022/ Thank you  Contact: – b.anderson@soton.ac.uk – @dataknut  Project: – www.energy.soton.ac.uk/tag/census2022/  Selected code: – github.com/dataknut/Census2022  Papers to date: – Claxton, R, J Reades, and B Anderson. (2012). ‘On the Value of Digital Traces for Commercial Strategy and Public Policy: Telecommunications Data as a Case Study’. In The Global Information Technology Report 2012, edited by S Dutta and B Bilbao-Osorio. Geneva: World Economic Forum. – Newing et al (2015) ‘The Role of Digital Trace Data in Supporting the Collection of Population Statistics - the Case for Smart Metered Electricity Consumption Data’. Population, Space and Place, July, doi:10.1002/psp.1972. 33

Editor's Notes

  1. If we just tried to use gas data we wouldn’t know anything about the red dots…
  2. If we just tried to use gas data we wouldn’t know anything about the red dots…
  3. There is some history of trying to do this in the energy demand literature – but to try to understand patterns of demand, not for official statistics