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AuthorAlsalemi, Abdullah
AuthorAl-Kababji, Ayman
AuthorHimeur, Yassine
AuthorBensaali, Faycal
AuthorAmira, Abbes
Available date2022-12-29T07:34:43Z
Publication Date2020
Publication NameProceedings - 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing, UCC 2020
ResourceScopus
URIhttp://dx.doi.org/10.1109/UCC48980.2020.00066
URIhttp://hdl.handle.net/10576/37824
AbstractEnergy efficiency is a crucial factor in the wellbeing of our planet. In parallel, Machine Learning (ML) plays an instrumental role in automating our lives and creating convenient workflows for enhancing behavior. So, analyzing energy behavior can help understand weak points and lay the path towards better interventions. Moving towards higher performance, cloud platforms can assist researchers in conducting classification trials that need high computational power. Under the larger umbrella of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 framework, we aim to influence consumers' behavioral change via improving their power consumption consciousness. In this paper, common cloud artificial intelligence platforms are benchmarked and compared for micromoment classification. Amazon Web Services, Google Cloud Platform, Google Colab, and Microsoft Azure Machine Learning are employed on simulated and real energy consumption datasets. The KNN, DNN, and SVM classifiers have been employed. Superb performance has been observed in the selected cloud platforms, showing relatively close performance. Yet, the nature of some algorithms limits the training performance. 2020 IEEE.
SponsorACKNOWLEDGMENT This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCloud
Data classification
Dataset
Energy efficiency
Platform
Realtime
Study
TitleCloud energy micro-moment data classification: A platform study
TypeConference Paper
Pagination420-425
dc.accessType Abstract Only


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