1. Exploration of electricity usage data from smart meters to investigate household composition Paula.Carroll@ucd.ie John.Dunne@cso.ie Michael.Hanley@ucdconnect.ie Tadhg.Murphy.1@ucdconnect.ie Topic (v): Integration and management of new data sources Seminar on Statistical Data Collection Geneva, Switzerland, 25-27 September 2013

2. Overview Setting the scene The data Problem statement The methodology Some results The resources Team review CSO review Concluding remarks 2

3. Setting the Scene -the players 3

4. The data Over 5000 households in pilot 3 months baseline data (reading every 30 mins) Pre-trial survey using CATI Purpose : Consumer Behaviour Trials in 2009 and 2010 4

5. Problem statement To determine household composition using smart metering data CategoryAdultsChildren A32 B31 C30 D25 E24 F23 G22 H21 I20 J11 K10 L41 M40 N51 O50 P60 5

6. The methodology Machine learning algorithms for classifier – (learning and testing || generalisation) – Neural Networks used – Binomial and Multinomial classification – Unbalanced data Data reduction/ dimension reduction – Used 21 explanatory variables as input to classifier – Variables normalised 6

7. Some results – balanced multinomial classifier Test Predicted Household category BCFGHIKMΣ % Accuracy Actual Household category B 00660620200.0 C 004101311200.0 F 008604202040.0 G 005218402010.0 H 00441740205.0 I 101208802040.0 K 0000051502075.0 M 001040330200.0 CategoryAdultsChildren A32 B31 C30 D25 E24 F23 G22 H21 I20 J11 K10 L41 M40 N51 O50 P60 “Confusion matrix” 7

8. The resources Project team of two persons for 3 months – Significant amount of time spent manipulating data Software: R with nnet and neuralnet packages Hardware: Required considerable computer resources for manipulating full dataset (Stokes at ICHEC) 8

9. Team review Problem statement too specific - broaden to household characteristics Alternative approach (cluster analysis and then describe clusters) Other techniques – PCA or signal processing 9

10. CSO review – forward looking Assuming go live 1.5m household meters linked to statistical household register in 2019 Existing statistical needs – Field force management – Auxiliary information – Sample selection /Representivity analysis New statistical products? – Energy consumption patterns by location, household etc – Quality of life (time to rise, time to bed) 10

11. Concluding remarks 3 V’s + V for Value – Is there value in SMD Access v Privacy – Legal, moral, proportionality Infrastructure for Big data ( 1.5m data points every 30 mins ) – Outsourcing, downsampling New tools, skills, approaches Roadmap – collaboration with suitable partners 11