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Household Classification Using Smart Meter Data

Author(s)
Carroll, Paula  
Murphy, Tadhg  
Hanley, Michael  
Dempsey, Daniel  
Dunne, John  
Uri
http://hdl.handle.net/10197/10383
Date Issued
2018-03-01
Date Available
2019-05-09T10:22:44Z
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.
Type of Material
Journal Article
Publisher
Sciendo
Journal
Journal of Official Statistics
Volume
34
Issue
1
Start Page
1
End Page
25
Copyright (Published Version)
2018 the Authors
Subjects

Neural network

Elastic net logistic ...

Classification system...

Household composition...

Smart meter data

DOI
10.1515/jos-2018-0001
Language
English
Status of Item
Peer reviewed
ISSN
0282-423X
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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Carroll_Estimating Household Composition from Smart Meter Data_JOS_Sept2017_preprint.pdf

Size

854.83 KB

Format

Adobe PDF

Checksum (MD5)

4fab54f8b9c335227e8f5742c7eb9650

Owning collection
Business Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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