Census2022: Extracting value from domestic consumption data in a postcensus era
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This document discusses a project called Census2022 that aims to extract value from domestic consumption data from smart meters in a post-census era. It details how smart meter data at high temporal resolution could be aggregated to small geographic areas to generate household statistics and indicators. The document then describes a study conducted with smart meter-like household electricity consumption data from 180 UK homes. Preliminary analysis of load profile indicators showed differences between households of varying sizes and employment statuses. However, more complex models are needed to better predict household characteristics from electricity use alone. Future steps involve accessing larger datasets and creating novel energy-based social indicators.
Census2022: Extracting value from domestic consumption data in a postcensus era
- 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. 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. 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. 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. Census2022: Extracting value from domestic… BEHAVE Sept 2014 In an ideal world… 5 This project
- 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. 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. Census2022: Extracting value from domestic… BEHAVE Sept 2014 Analytically: Load profile indicators 8
- 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. 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. 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. 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