Producing and validating small area estimates of household electricity demand
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The document discusses the production and validation of small area estimates of household electricity demand, highlighting the need for detailed data to improve infrastructure planning and energy efficiency interventions. It outlines methodologies for estimation, validation processes, and preliminary results indicating consumption patterns and inequalities. Future directions include updating data for 2011 and using measured energy consumption for better accuracy.
Producing and validating small area estimates of household electricity demand
- 1. Producing and validating small area estimates of household electricity demand Dr Ben Anderson Sustainable Energy Research Centre University of Southampton @dataknut IMA 2013 Canberra, December 12th 2013
- 2. Small Area Estimates of Electricity Consumption Contents What & Why How? Results – Overall consumption – Consumption inequalities Conclusions & future Directions @dataknut 2
- 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 4
- 5. Small Area Estimates of Electricity Consumption Digression: What’s a small area? In this case… – English Lower Layer Super Output Areas – Census 2001 LSOAs – c. 630 households each – 148 in Southampton City @dataknut 5
- 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 – Politics! @dataknut 6
- 7. Small Area Estimates of Electricity Consumption The problem: Small area summaries exist But they are aggregates – Or averages And we want a micro-level model – To micro-simulate change… @dataknut 7
- 8. Small Area Estimates of Electricity Consumption What can we do? A bespoke energy census – ££££££££££££££££££££ @dataknut 8
- 9. Small Area Estimates of Electricity Consumption What can we do? A bespoke energy census – ££££££££££££££££££££ A large sample energy survey covering all LSOAs – ££££££££££ @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 – ££££££££££ Small Area Estimation – Take existing area level data – Take (ideally) an existing large n survey – Combine £ @dataknut 10
- 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 11
- 12. Small Area Estimates of Electricity Consumption Contents What & Why Estimation How? Results – Overall consumption – Consumption inequalities Conclusions & Future Directions @dataknut 12
- 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 – Projected ‘surveys’ – Projected ‘census’ So far… @dataknut 13
- 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 @dataknut Weights LSOA 2.1 (Region2) Iterative proportional fitting Ballas et al (2005) 14
- 15. Small Area Estimates of Electricity Consumption Key First Job: Choose your constraints Census data Survey data You may have little choice @dataknut 16
- 16. Small Area Estimates of Electricity Consumption Key First Job: 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 19
- 17. Small Area Estimates of Electricity Consumption IPF… Well known! Deming and Stephan 1940 – Fienberg 1970; Wong 1992 A way of iteratively adjusting statistical tables – To give known margins (row/column totals) In this case – Create weights for each case so LSOA totals ‘fit’ constraints – Weighting ‘down’ @dataknut 20
- 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 21
- 19. Small Area Estimates of Electricity Consumption Preliminary results: Electricity Mean weekly £ Modelled Census 2001 LC&F Survey 20082010 @dataknut 22
- 20. Small Area Estimates of Electricity Consumption Validation: Electricity Mean weekly £ Observed @LSOA DECC 2010 Spearman: 0.317 @dataknut 23
- 21. Small Area Estimates of Electricity Consumption Preliminary results: Electricity Total weekly £ Modelled Census 2001 LC&F Survey 2008-2010 @dataknut 24
- 22. Small Area Estimates of Electricity Consumption Validation: Electricity Total weekly £ Observed @LSOA DECC 2010 Spearman: 0.509 @dataknut 25
- 23. Small Area Estimates of Electricity Consumption What is causing the error? Heating! – @dataknut 2011 data 26
- 24. Small Area Estimates of Electricity Consumption What is causing the error? Heating! – 2011 data Housing growth Combined @dataknut 27
- 25. Small Area Estimates of Electricity Consumption Consumption inequality Area level gini R = -0.413 – @dataknut (p < 0.001) 28
- 26. Small Area Estimates of Electricity Consumption Consumption inequality Area level gini R = 0.463 – @dataknut (p < 0.001) 29
- 27. Small Area Estimates of Electricity Consumption My big worry Data quality @dataknut 30
- 28. Small Area Estimates of Electricity Consumption Contents What & Why How? Results – Overall consumption – Consumption inequalities Conclusions & Future Directions @dataknut 31
- 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 32
- 30. Small Area Estimates of Electricity Consumption Future directions Update for 2011 data Census projection 1981 -> 2021 Use measured energy consumption Contact: – b.anderson@soton.ac.uk @dataknut 33
Editor's Notes
- #31: Data from SPRG linked water demand survey2 implications:Error in the estimates (spurious correlation with constraints)Error in any policy microsimulation