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

Electricity consumption and household characteristics: implications for census-taking in a smart metered future
Electricity consumption and household characteristics: implications for census-taking in a smart metered future
This paper assesses the feasibility of determining key household characteristics based on temporal load profiles of household electricity demand. It is known that household characteristics, behaviours and routines drive a number of features of household electricity loads in ways which are currently not fully understood. The roll out of domestic smart meters in the UK and elsewhere could enable better understanding through the collection of high temporal resolution electricity monitoring data at the household level. Such data affords tremendous potential to invert the established relationship between household characteristics and temporal load profiles. Rather than use household characteristics as a predictor of loads, observed electricity load profiles, or indicators based on them, could instead be used to impute household characteristics. These micro level imputed characteristics could then be aggregated at the small area level to produce ‘census- like’ small area indicators. This work briefly reviews the nature of current and future census taking in the UK before outlining the household characteristics that are to be found in the UK census and which are also known to influence electricity load profiles. It then presents descriptive analysis of two smart meter-like datasets of half-hourly domestic electricity consumption before reporting on the results from a multilevel modelling-based analysis of the same data. The work concludes that a number of household characteristics of the kind to be found in UK census-derived small area statistics may be predicted from particular load profile indicators. A discussion of the steps required to test and validate this approach and the wider implications for census taking is also provided.
census2022
0198-9715
1-10
Anderson, Ben
01e98bbd-b402-48b0-b83e-142341a39b2d
Lin, Sharon
e3661a39-afd4-4929-9c4f-aede2e43a022
Newing, Andy
ac2b2cfd-a2e9-45e8-8a1c-19521163aae6
Bahaj, Abubakr
a64074cc-2b6e-43df-adac-a8437e7f1b37
James, Patrick
da0be14a-aa63-46a7-8646-a37f9a02a71b
Anderson, Ben
01e98bbd-b402-48b0-b83e-142341a39b2d
Lin, Sharon
e3661a39-afd4-4929-9c4f-aede2e43a022
Newing, Andy
ac2b2cfd-a2e9-45e8-8a1c-19521163aae6
Bahaj, Abubakr
a64074cc-2b6e-43df-adac-a8437e7f1b37
James, Patrick
da0be14a-aa63-46a7-8646-a37f9a02a71b

Anderson, Ben, Lin, Sharon, Newing, Andy, Bahaj, Abubakr and James, Patrick (2016) Electricity consumption and household characteristics: implications for census-taking in a smart metered future. Computers, Environment and Urban Systems, 1-10. (doi:10.1016/j.compenvurbsys.2016.06.003).

Record type: Article

Abstract

This paper assesses the feasibility of determining key household characteristics based on temporal load profiles of household electricity demand. It is known that household characteristics, behaviours and routines drive a number of features of household electricity loads in ways which are currently not fully understood. The roll out of domestic smart meters in the UK and elsewhere could enable better understanding through the collection of high temporal resolution electricity monitoring data at the household level. Such data affords tremendous potential to invert the established relationship between household characteristics and temporal load profiles. Rather than use household characteristics as a predictor of loads, observed electricity load profiles, or indicators based on them, could instead be used to impute household characteristics. These micro level imputed characteristics could then be aggregated at the small area level to produce ‘census- like’ small area indicators. This work briefly reviews the nature of current and future census taking in the UK before outlining the household characteristics that are to be found in the UK census and which are also known to influence electricity load profiles. It then presents descriptive analysis of two smart meter-like datasets of half-hourly domestic electricity consumption before reporting on the results from a multilevel modelling-based analysis of the same data. The work concludes that a number of household characteristics of the kind to be found in UK census-derived small area statistics may be predicted from particular load profile indicators. A discussion of the steps required to test and validate this approach and the wider implications for census taking is also provided.

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More information

Accepted/In Press date: 15 June 2016
e-pub ahead of print date: 1 July 2016
Keywords: census2022
Organisations: Energy & Climate Change Group

Identifiers

Local EPrints ID: 396943
URI: https://eprints.soton.ac.uk/id/eprint/396943
ISSN: 0198-9715
PURE UUID: ff2277cc-c2a2-4499-bdbc-cc6ac6381a07
ORCID for Ben Anderson: ORCID iD orcid.org/0000-0003-2092-4406
ORCID for Abubakr Bahaj: ORCID iD orcid.org/0000-0002-0043-6045

Catalogue record

Date deposited: 17 Jun 2016 10:15
Last modified: 15 Aug 2019 05:26

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Contributors

Author: Ben Anderson ORCID iD
Author: Sharon Lin
Author: Andy Newing
Author: Abubakr Bahaj ORCID iD
Author: Patrick James

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