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AuthorAlsalemi, Abdullah
AuthorHimeur, Yassine
AuthorBensaali, Fayal
AuthorAmira, Abbes
AuthorSardianos, Christos
AuthorVarlamis, Iraklis
AuthorDimitrakopoulos, George
Available date2022-12-29T07:34:44Z
Publication Date2020
Publication NameIEEE Access
ResourceScopus
URIhttp://dx.doi.org/10.1109/aCCESS.2020.2966640
URIhttp://hdl.handle.net/10576/37827
AbstractExcessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. a large amount of research has been directed towards behavioral change for energy efficiency. Thus, it is prudent to develop an intelligent system that combines the proper use of technology with behavior change research in order to sustainably transform end-user behavior at a large scale. This paper presents an overview of our aI-based energy efficiency framework for domestic applications and explains how micro-moments can provide an accurate understanding of user behavior and lead to more effective recommendations. Micro-moments are short-term events at which an energy-saving recommendation is presented to the consumer. They are detected using a variety of sensing modules placed at prominent locations in the household. a supervised machine learning classifier is then used to analyze the acquired micro-moments, identify abnormalities, and formulate a list of energy-saving recommendations. Each recommendation is presented through the end-user mobile application. The results so far include a mobile application in the front-end and a set of sensing modules in the backend that comprise, an ensemble bagging-trees micro-moment classifier (achieving up to 99.64% accuracy and 98.8% F-score), and a recommendation engine. 2013 IEEE.
SponsorThe statements made herein are solely the responsibility of the authors. This work was supported in part by the National Priorities Research Program (NPRP) from the Qatar National Research Fund (a member of Qatar Foundation) under Grant 10-0130-170288.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectClassification
data visualization
domestic energy usage
energy efficiency
micro-moment
mobile application
recommender system
Titleachieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations
TypeArticle
Pagination15047-15055
Volume Number8
dc.accessType Open Access


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