Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Census2022: Extracting value from domestic consumption data in a post­census era

511 views

Published on

Andy Newing a.newing@soton.ac.uk
Ben Anderson b.anderson@soton.ac.uk (@dataknut)
10 minute 'lightning' paper presented at BEHAVE 2014, Said Business School, Oxford, 4th September 2014.

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

Census2022: Extracting value from domestic consumption data in a post­census era

  1. 1. Census2022: Extracting value from domestic consumption data in a post­census era BEHAVE conference – September 2014 Andy Newing a.newing@soton.ac.uk Ben Anderson b.anderson@soton.ac.uk (@dataknut) Sustainable Energy Research Group
  2. 2. Census2022: Extracting value from domestic… BEHAVE Sept 2014 What we are trying to do: Census2022  UK Census 2011/2021 evolution  Timeliness & cost  Challenges  Finding new ways to deliver the Census – ‘Census-like’  Opportunities  New kinds of data  New kinds of social indicators - ‘Census-plus’  More frequently 2
  3. 3. Census2022: Extracting value from domestic… BEHAVE Sept 2014 Smart metering • Universal mandate • Geo-coded • Doesn’t ‘lie’ • (but may be errors/’missing’) • High temporal resolution • Near 100% coverage • Especially for electricity 3 Crucial!!
  4. 4. Census2022: Extracting value from domestic… BEHAVE Sept 2014 Generating area based household statistics and indicators Household Load Profiles Infer household characteristics Aggregate to small area geographies
  5. 5. Census2022: Extracting value from domestic… BEHAVE Sept 2014 In an ideal world… 5 This project
  6. 6. Census2022: Extracting value from domestic… BEHAVE Sept 2014 UoS Energy Monitoring Study (UoS-E) 6  Smart meter-like household dataset  n=180  Repeated surveys:  characteristics, behaviors and attitudes  1 second level power import  Sample: October 2011  ~ 500m records (1 second)  Cleaned & checked  Aggregated (mean power)  ~ 250,000 records (half hourly)
  7. 7. Census2022: Extracting value from domestic… BEHAVE Sept 2014 7 Descriptive Analysis 1-2 persons vs 3+ Midweek: No children vs 1-2 vs 3+ Midweek: Respondent in employment vs not
  8. 8. Census2022: Extracting value from domestic… BEHAVE Sept 2014 Analytically: Load profile indicators 8
  9. 9. Census2022: Extracting value from domestic… BEHAVE Sept 2014 Evening consumption factor (ECF) Midweek (Tuesday – Thursday) Ratio of mean 30 minute evening peak power import (4pm – 8pm) to off peak power import Ψ note: n= 5 9 ECF All households Employed Not in active employment All households 2.13 1.64 No Children 2.21 2.54 2.09 With Children 2.31 2.29 1.30Ψ
  10. 10. Census2022: Extracting value from domestic… BEHAVE Sept 2014 Predicting household characteristics • Exploratory linear regression Presumption of availability via administrative sources • clear links but low explanatory power
  11. 11. Census2022: Extracting value from domestic… BEHAVE Sept 2014 Conclusions & Next Steps • Value of pure load profile approaches unclear • More complex regression models needed • Exploration of ‘time series’approaches • Need: • Access to larger dataset with greater range of household types • Creation of ‘census-plus’ indicators • Novel energy consumption-based social indicators?
  12. 12. Census2022: Extracting value from domestic… BEHAVE Sept 2014 Thank you  http://www.energy.soton.ac.uk/category/research/energy-behaviour/ census-2022/  Ben Anderson b.anderson@soton.ac.uk (@dataknut)  Andy Newing a.newing@soton.ac.uk 12

×