Household Classification Using Smart Meter Data

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Title: Household Classification Using Smart Meter Data
Authors: Carroll, Paula
Murphy, Tadhg
Hanley, Michael
Dempsey, Daniel
Dunne, John
Permanent link: http://hdl.handle.net/10197/10383
Date: 1-Mar-2018
Online since: 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
Keywords: Neural networkElastic net logistic regressionClassification systemHousehold compositionSmart meter data
DOI: 10.1515/jos-2018-0001
Language: en
Status of Item: Peer reviewed
Appears in Collections:Business Research Collection

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