Household Classification Using Smart Meter Data

Carroll Paula (), Murphy Tadhg (), Hanley Michael (), Dempsey Daniel () and Dunne John ()
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Carroll Paula: Centre for Business Analytics, School of Business, University College Dublin, Belfield, Dublin 4, Dublin, Ireland
Murphy Tadhg: Centre for Business Analytics, School of Business, University College Dublin, Belfield, Dublin 4, Dublin, Ireland
Hanley Michael: Centre for Business Analytics, School of Business, University College Dublin, Belfield, Dublin 4, Dublin, Ireland
Dempsey Daniel: Centre for Business Analytics, School of Business, University College Dublin, Belfield, Dublin 4, Dublin, Ireland
Dunne John: Central Statistics Office, Skehard Road, Mahon, Cork, Ireland

Journal of Official Statistics, 2018, vol. 34, issue 1, 1-25

Abstract: This article describes a project conducted in conjunction with the Central Statistics Office of Ireland in response to a planned national rollout of smart electricity metering in Ireland. We investigate how this new data source might be used for the purpose of official statistics production. This study specifically looks at the question of determining household composition from electricity smart meter data using both Neural Networks (a supervised machine learning approach) and Elastic Net Logistic regression. An overview of both classification techniques is given. Results for both approaches are presented with analysis. We find that the smart meter data alone is limited in its capability to distinguish between household categories but that it does provide some useful insights.

Keywords: Neural network; elastic net logistic regression; classification system; household composition; smart meter data (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:34:y:2018:i:1:p:1-25:n:1

DOI: 10.1515/jos-2018-0001

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