research-article

Automatic socio-economic classification of households using electricity consumption data

Authors:

Christian Beckel,

Leyna Sadamori,

Silvia SantiniAuthors Info & Claims

e-Energy '13: Proceedings of the fourth international conference on Future energy systems

Pages 75 - 86

https://doi.org/10.1145/2487166.2487175

Published: 21 May 2013 Publication History

Abstract

Interest in analyzing electricity consumption data of private households has grown steadily in the last years. Several authors have for instance focused on identifying groups of households with similar consumption patterns or on providing feedback to consumers in order to motivate them to reduce their energy consumption. In this paper, we propose to use electricity consumption data to classify households according to pre-defined "properties" of interest. Examples of these properties include the floor area of a household or the number of its occupants. Energy providers can leverage knowledge of such household properties to shape premium services (e.g., energy consulting) for their customers. We present a classification system - called CLASS - that takes as input electricity consumption data of a private household and provides as output the estimated values of its properties. We describe the design and implementation of CLASS and evaluate its performance. To this end, we rely on electricity consumption traces from 3,488 private households, collected at a 30-minute granularity and for a period of more than 1.5 years. Our evaluation shows that CLASS - relying on electricity consumption data only - can estimate the majority of the considered household properties with more than 70% accuracy. For some of the properties, CLASS's accuracy exceeds 80%. Furthermore, we show that for selected properties the use of a priori information can increase classification accuracy by up to 11%.

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Cited By

Cretu ARusu Mde Montjoye YVilela JSchulmann HLi N(2024)Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling AttacksProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653272(295-306)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3626232.3653272
Zhang HWang ZLiu XZhou WDing YLi YHu J(2024)Power Network Smart Meter Data Driven Cross-Task Transfer Learning for Resident Characteristics EstimationIEEE Journal of Emerging and Selected Topics in Industrial Electronics10.1109/JESTIE.2024.33505375:2(652-661)Online publication date: Apr-2024
https://doi.org/10.1109/JESTIE.2024.3350537
Chadoulos SKoutsopoulos IPolyzos G(2023)Deep4Ener: Energy Demand forecasting for Unseen Consumers with Scarce Data Using a Single Deep Learning ModelACM SIGEnergy Energy Informatics Review10.1145/3607120.36071223:1(2-13)Online publication date: 30-Jun-2023
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Index Terms

Automatic socio-economic classification of households using electricity consumption data
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

e-Energy '13: Proceedings of the fourth international conference on Future energy systems

January 2013

306 pages

ISBN:9781450320528

DOI:10.1145/2487166

General Chairs:
David Culler
University of California, Berkeley, USA
,
Catherine Rosenberg
University of Waterloo, Canada
,
Program Chairs:
S. Keshav
University of Waterloo, Canada
,
Jim Kurose
University of Massachusetts, USA

Copyright © 2013 ACM.

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Published: 21 May 2013

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e-Energy '13: The Fourth International Conference on Future Energy Systems

May 21 - 24, 2013

California, Berkeley, USA

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e-Energy '13 Paper Acceptance Rate 40 of 76 submissions, 53%;

Overall Acceptance Rate 160 of 446 submissions, 36%

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Cited By

Cretu ARusu Mde Montjoye YVilela JSchulmann HLi N(2024)Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling AttacksProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653272(295-306)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3626232.3653272
Zhang HWang ZLiu XZhou WDing YLi YHu J(2024)Power Network Smart Meter Data Driven Cross-Task Transfer Learning for Resident Characteristics EstimationIEEE Journal of Emerging and Selected Topics in Industrial Electronics10.1109/JESTIE.2024.33505375:2(652-661)Online publication date: Apr-2024
https://doi.org/10.1109/JESTIE.2024.3350537
Chadoulos SKoutsopoulos IPolyzos G(2023)Deep4Ener: Energy Demand forecasting for Unseen Consumers with Scarce Data Using a Single Deep Learning ModelACM SIGEnergy Energy Informatics Review10.1145/3607120.36071223:1(2-13)Online publication date: 30-Jun-2023
https://dl.acm.org/doi/10.1145/3607120.3607122
Tsiporkova EVis MKlein SHristoskova ABoeva V(2023)Mitigating Concept Drift in Distributed Contexts with Dynamic Repository of Federated Models2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386236(2690-2699)Online publication date: 15-Dec-2023
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Fang HXiao JWang Y(2023)Self-training convolutional autoencoder for consumer characteristics identification with imbalance datasetsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106605124:COnline publication date: 20-Sep-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106605
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Biansoongnern SPlangklang B(2022)An Alternative Low-Cost Embedded NILM System for Household Energy Conservation with a Low Sampling RateSymmetry10.3390/sym1402027914:2(279)Online publication date: 29-Jan-2022
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Bouktif SOuni ALazarova-Molnar S(2022)Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An OverviewEnergies10.3390/en1505174115:5(1741)Online publication date: 25-Feb-2022
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Wang YWang QWu ZZhu B(2022)Socio-demographic Information Extraction from Load Profile Using Convolutional Neural NetworkProceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)10.2991/978-94-6463-010-7_72(703-715)Online publication date: 2-Dec-2022
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