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

1. 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

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

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

4. Small Area Estimates of Electricity Consumption Digression: Geography @dataknut 4  Southampton (UK)

5. Small Area Estimates of Electricity Consumption 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

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? – Infrastructure planning – Energy efficiency intervention analysis – Energy inequality analysis – Politics! @dataknut 6

7. Small Area Estimates of Electricity Consumption 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

8. Small Area Estimates of Electricity Consumption 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

9. Small Area Estimates of Electricity Consumption What can we do?  A bespoke energy census – ££££££££££££££££££££ @dataknut 9

10. Small Area Estimates of Electricity Consumption What can we do?  A bespoke energy census – ££££££££££££££££££££  A large sample energy survey covering all LSOAs – ££££££££££ @dataknut 10

11. 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 11

12. Small Area Estimates of Electricity Consumption 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/

13. Small Area Estimates of Electricity Consumption Contents  What & Why  How?  Results – Overall consumption – Consumption inequalities – Temporal profiles  Conclusions & Future Directions @dataknut 13 Estimation

14. Small Area Estimates of Electricity Consumption 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)

15. Small Area Estimates of Electricity Consumption 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

16. Small Area Estimates of Electricity Consumption Key First Job:  Choose your constraints @dataknut 16 Census data Survey data  How? – Selection via regression in micro data – You may have little choice

17. Small Area Estimates of Electricity Consumption ‘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’

18. Small Area Estimates of Electricity Consumption Contents  What & Why  How?  Results – Overall consumption – Consumption inequalities – Temporal profiles  Conclusions & Future Directions @dataknut 18

19. Small Area Estimates of Electricity Consumption 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

20. Small Area Estimates of Electricity Consumption 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%

21. Small Area Estimates of Electricity Consumption Preliminary results: Electricity  Mean weekly household £  Modelled  Census 2001  LC&F Survey 2008- 2010 @dataknut 21

22. Small Area Estimates of Electricity Consumption Validation: Electricity  Mean weekly household £  Observed @LSOA – DECC 2010  Spearman: 0.317 @dataknut 22

23. Small Area Estimates of Electricity Consumption Preliminary results: Electricity  Total weekly household £  Modelled  Census 2001  LC&F Survey 2008-2010 @dataknut 23

24. Small Area Estimates of Electricity Consumption Validation: Electricity  Total weekly household £  Observed @LSOA – DECC 2010  Spearman: 0.509 @dataknut 24

25. Small Area Estimates of Electricity Consumption What is causing the error?  Housing growth Model overestimates @dataknut 25 Model underestimates

26. Small Area Estimates of Electricity Consumption What is causing the error?  Heating! – 2011 data Model overestimates  Combined: – Heating = 60% – Growth = 5% @dataknut 26 Model underestimates

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

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

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

30. 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 30

32. Small Area Estimates of Electricity Consumption 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

33. Small Area Estimates of Electricity Consumption 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

34. Small Area Estimates of Electricity Consumption 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+

35. Small Area Estimates of Electricity Consumption 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)

36. Small Area Estimates of Electricity Consumption 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 Mean consumption per half houir (Watts) 25-64 65+ Number of earners

37. Small Area Estimates of Electricity Consumption 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 Mean consumption per half houir (Watts) 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 Mean consumption per half houir (Watts) 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) 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

38. Small Area Estimates of Electricity Consumption 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) 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 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 Sum of watts per half hour 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

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

40. Small Area Estimates of Electricity Consumption 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 Sum of watts per half hour earners earners earner earners Now the network sees.. 40 Source: Author’s calculations using UK Time Use Survey 2000/1 [http://discover.ukdataservice.ac.uk/catalogue/?sn=4504], weighted) and Model 2 power assumptions

41. Small Area Estimates of Electricity Consumption 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)

42. Small Area Estimates of Electricity Consumption Key First Job:  Choose your constraints @dataknut 42 Census data Survey data  How? – Regression selection methods? – Whatever is available!

43. Small Area Estimates of Electricity Consumption 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?

44. Small Area Estimates of Electricity Consumption 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 Sum of watts per half hour 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 Sum of watts per half hour 3+ 2 1 0 LSOA E01017139: highest % of households with 0 earners in Southampton LSOA E01017180: lowest % of households with 0 earners in Southampton

45. Small Area Estimates of Electricity Consumption Results (Model 1) @dataknut 45 Sum of half hourly power consumption (winter 2000/1) 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. Map created in R (ggmap)

46. Small Area Estimates of Electricity Consumption 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 earners in Southampton earners in Southampton 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 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 Sum of watts per half hour 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 Sum of watts per half hour 3+ 2 1 0

48. Small Area Estimates of Electricity Consumption 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??

49. Small Area Estimates of Electricity Consumption 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

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

Ben Anderson

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