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

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Presentation given at MRS Workshop "Can Big Data replace the Census? What does Big Data give us now?" , March 7, 2016, MRS, London (https://www.mrs.org.uk/event/conferences/can_big_data_replace_the_census/course/4088/id/10035)

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

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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