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Pitfalls of Machine Learning Methods in Smart Grids: A Legal Perspective

  • The widespread implementation of smart meters (SM) and the deployment of the advanced metering infrastructure (AMI) provide large amounts of fine-grained data on prosumers. Machine learning (ML) algorithms are used in different techniques, e.g. non-intrusive load monitoring (NILM), to extract useful information from collected data. However, the use of ML algorithms to gain insight on prosumer behavior and characteristics raises not only numerous technical but also legal concerns. This paper maps electricity prosumer concerns towards the AMI and its ML based analytical tools in terms of data protection, privacy and cybersecurity and conducts a legal analysis of the identified prosumer concerns within the context of the EU regulatory frameworks. By mapping the concerns referred to in the technical literature, the main aim of the paper is to provide a legal perspective on those concerns. The output of this paper is a visual tool in form of a table, meant to guide prosumers, utility, technology and energy service providers. It shows theThe widespread implementation of smart meters (SM) and the deployment of the advanced metering infrastructure (AMI) provide large amounts of fine-grained data on prosumers. Machine learning (ML) algorithms are used in different techniques, e.g. non-intrusive load monitoring (NILM), to extract useful information from collected data. However, the use of ML algorithms to gain insight on prosumer behavior and characteristics raises not only numerous technical but also legal concerns. This paper maps electricity prosumer concerns towards the AMI and its ML based analytical tools in terms of data protection, privacy and cybersecurity and conducts a legal analysis of the identified prosumer concerns within the context of the EU regulatory frameworks. By mapping the concerns referred to in the technical literature, the main aim of the paper is to provide a legal perspective on those concerns. The output of this paper is a visual tool in form of a table, meant to guide prosumers, utility, technology and energy service providers. It shows the areas that need increased attention when dealing with specific prosumer concerns as identified in the technical literature.show moreshow less

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Metadaten
Author:Alexander AntonovORCiDGND, Tobias HäringORCiD, Tarmo KorotkoORCiD, Argo RosinORCiD, Tanel KerikmäeORCiDGND, Helmuth Biechl
DOI:https://doi.org/10.1109/ISCSIC54682.2021.00053
Identifier:978-1-6654-1627-6 OPAC HS OPAC extern
Identifier:978-1-6654-1628-3 OPAC HS OPAC extern
Publisher:IEEE
Place of publication:New York
Document Type:conference proceeding (article)
Conference:2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), 12-14 November 2021, Rome, Italy
Language:English
Date of Publication (online):2021/12/23
Year of first Publication:2021
Tag:Cybersecurity; Data protection; EU; GDPR; Law; Machine learning; Machine learning algorithms; Smart City; Smart grids; Smart meters; Visualization
Volume:2021
Number of pages:9 Seiten
First Page:248
Last Page:256
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
Research focus:FSP4: Soziale Innovationen
Publication Lists:Biechl, Helmuth
Publication reviewed:begutachtet
Release Date:2024/02/19
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