research-article

Symbolic representation of smart meter data

Authors:

Tri Kurniawan Wijaya,

Karl AbererAuthors Info & Claims

EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops

Pages 242 - 248

https://doi.org/10.1145/2457317.2457357

Published: 18 March 2013 Publication History

Abstract

Currently smart meter data analytics has received enormous attention because it allows utility companies to analyze customer consumption behavior in real time. However, the amount of data generated by these sensors is very large. As a result, analytics performed on top of it become very expensive. Furthermore, smart meter data contains very detailed energy consumption measurement which can lead to customer privacy breach and all risks associated with it. In this work, we address the problem on how to reduce smart meter data numerosity and its detailed measurement while maintaining its analytics accuracy. We convert the data into symbolic representation and allow various machine learning algorithms to be performed on top of it. In addition, our symbolic representation admit an additional advantage to allow also algorithms which usually work on nominal and string to be run on top of smart meter data. We provide an experiment for classification and forecasting tasks using real-world data. And finally, we illustrate several directions to extend our work further.

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Cited By

Liu BHou YLuan WLiu ZChen SYu Y(2023)A divide-and-conquer method for compression and reconstruction of smart meter dataApplied Energy10.1016/j.apenergy.2023.120851336(120851)Online publication date: Apr-2023
https://doi.org/10.1016/j.apenergy.2023.120851
Wang HRao HGong XChen ZMao DZhang J(2022)Electric Power Asynchronous Heterogeneous Data Accelerated Compression for Edge Computing2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)10.1109/SmartCloud55982.2022.00025(121-126)Online publication date: Oct-2022
https://doi.org/10.1109/SmartCloud55982.2022.00025
Jadhav RMahajan A(2022)Review on Data Compression Methods of Smart Grid Power System Using Wavelet TransformSmart Energy and Advancement in Power Technologies10.1007/978-981-19-4971-5_18(237-255)Online publication date: 9-Nov-2022
https://doi.org/10.1007/978-981-19-4971-5_18
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Symbolic representation of smart meter data

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cover image ACM Other conferences

EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops

March 2013

423 pages

ISBN:9781450315999

DOI:10.1145/2457317

General Chair:
Giovanna Guerrini
Università di Genova, Italy

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2013

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EDBT/ICDT '13

EDBT/ICDT '13: Joint 2013 EDBT/ICDT Conferences

March 18 - 22, 2013

Genoa, Italy

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EDBT '13 Paper Acceptance Rate 7 of 10 submissions, 70%;

Overall Acceptance Rate 7 of 10 submissions, 70%

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Cited By

Liu BHou YLuan WLiu ZChen SYu Y(2023)A divide-and-conquer method for compression and reconstruction of smart meter dataApplied Energy10.1016/j.apenergy.2023.120851336(120851)Online publication date: Apr-2023
https://doi.org/10.1016/j.apenergy.2023.120851
Wang HRao HGong XChen ZMao DZhang J(2022)Electric Power Asynchronous Heterogeneous Data Accelerated Compression for Edge Computing2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)10.1109/SmartCloud55982.2022.00025(121-126)Online publication date: Oct-2022
https://doi.org/10.1109/SmartCloud55982.2022.00025
Jadhav RMahajan A(2022)Review on Data Compression Methods of Smart Grid Power System Using Wavelet TransformSmart Energy and Advancement in Power Technologies10.1007/978-981-19-4971-5_18(237-255)Online publication date: 9-Nov-2022
https://doi.org/10.1007/978-981-19-4971-5_18
Ward JBarker S(2020)PowerstripProceedings of the Eleventh ACM International Conference on Future Energy Systems10.1145/3396851.3397716(242-252)Online publication date: 12-Jun-2020
https://dl.acm.org/doi/10.1145/3396851.3397716
Calegari RCiatto GMariani SDenti EOmicini A(2018)LPaaS as Micro-Intelligence: Enhancing IoT with Symbolic ReasoningBig Data and Cognitive Computing10.3390/bdcc20300232:3(23)Online publication date: 3-Aug-2018
https://doi.org/10.3390/bdcc2030023
Wen LZhou KYang SLi L(2018)Compression of smart meter big data: A surveyRenewable and Sustainable Energy Reviews10.1016/j.rser.2018.03.08891(59-69)Online publication date: Aug-2018
https://doi.org/10.1016/j.rser.2018.03.088
Albrecht SFritz MStrüker JZiekow H(2017)Targeting customers for an optimized energy procurementComputer Science - Research and Development10.1007/s00450-016-0303-x32:1-2(225-235)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s00450-016-0303-x
Liu XGolab LGolab WIlyas IJin S(2016)Smart Meter Data AnalyticsACM Transactions on Database Systems10.1145/300429542:1(1-39)Online publication date: 21-Nov-2016
https://dl.acm.org/doi/10.1145/3004295
Li DLi LBissyandé TKlein JLe Traon Y(2016)DSCo: A Language Modeling Approach for Time Series ClassificationMachine Learning and Data Mining in Pattern Recognition10.1007/978-3-319-41920-6_22(294-310)Online publication date: 28-Jun-2016
https://doi.org/10.1007/978-3-319-41920-6_22
Eberle JWijaya TAberer K(2015)Online unsupervised state recognition in sensor data2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM.2015.7146506(29-36)Online publication date: Mar-2015
https://doi.org/10.1109/PERCOM.2015.7146506
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