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AuthorAly, Hussein
AuthorAl-Ali, Abdulaziz
AuthorAl-Ali, Abdulla
AuthorMalluhi, Qutaibah
Available date2024-07-17T07:14:42Z
Publication Date2023
Publication NameIEEE PES Innovative Smart Grid Technologies Conference Europe
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ISGTEUROPE56780.2023.10407215
URIhttp://hdl.handle.net/10576/56744
AbstractThe Smart Grid Advanced Metering Infrastructure (AMI) has revolutionized the smart grid network, generating vast amounts of data that can be utilized for diverse objectives, one of which is Household Characteristics Classification (HCC). This can help the utility provider profile their customers and tailor their services to meet customer needs. To accomplish this task, we evaluated multiple Machine learning HCC models, with a focus on CNN-based models due to their wide popularity in the field of smart grid signal classification. We evaluated 1D and 2D variants of four different CNN architectures. Our experimental analysis revealed that ResNet-based models achieved the best performance on the task of HCC. Also, we found that 2D models tends to perform better than 1D variants.
SponsorThis publication was made possible by NPRP grant 12C-0814-190012 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the authors.
Languageen
PublisherIEEE Computer Society
SubjectCNN architectures
Convolutional Neural Network
Electricity Data
Private Smart Grid
Smart Grid Data
Socio-demographic Classification
TitleAnalysis of Predictive Models for Revealing Household Characteristics using Smart Grid Data
TypeConference
Pagination-
dc.accessType Abstract Only


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