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المؤلفSayed, Aya
المؤلفHimeur, Yassine
المؤلفAlsalemi, Abdullah
المؤلفBensaali, Faycal
المؤلفAmira, Abbes
تاريخ الإتاحة2022-12-29T07:34:46Z
تاريخ النشر2022
اسم المنشورIEEE Systems Journal
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/JSYST.2021.3124793
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37853
الملخصPreserving energy in households and office buildings is a significant challenge, mainly due to the recent shortage of energy resources, the uprising of the current environmental problems, and the global lack of utilizing energy-saving technologies. Not to mention, within some regions, COVID-19 social distancing measures have led to a temporary transfer of energy demand from commercial and urban centers to residential areas, causing an increased use and higher charges, and in turn, creating economic impacts on customers. Therefore, the marketplace could benefit from developing an Internet of Things ecosystem that monitors energy consumption habits and promptly recommends action to facilitate energy efficiency. This article aims to present the full integration of a proposed energy efficiency framework into the Home-Assistant platform using an edge-based architecture. End users can visualize their consumption patterns as well as ambient environmental data using the Home-Assistant user interface. More notably, explainable energy-saving recommendations are delivered to end users in the form of notifications via the mobile application to facilitate habit change. In this context, to the best of the authors' knowledge, this is the first attempt to develop and implement an energy-saving recommender system on edge devices. Thus, ensuring better privacy preservation since data are processed locally on the edge, without the need to transmit them to remote servers, as is the case with cloudlet platforms. 2022 IEEE.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعBig data
COVID-19
data visualization
energy efficiency
Home-Assistant
Internet of Energy (IoE)
recommender system (RS)
sensing system
smart plug
العنوانIntelligent Edge-Based Recommender System for Internet of Energy Applications
النوعArticle
الصفحات5001-5010
رقم العدد3
رقم المجلد16
dc.accessType Abstract Only


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