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AuthorVarlamis, Iraklis
AuthorSardianos, Christos
AuthorChronis, Christos
AuthorDimitrakopoulos, George
AuthorHimeur, Yassine
AuthorAlsalemi, Abdullah
AuthorBensaali, Faycal
AuthorAmira, Abbes
Available date2022-12-29T07:34:42Z
Publication Date2022
Publication NameApplied Energy
AbstractDespite the variety of sensors that can be used in a smart home or office setup, for monitoring energy consumption and assisting users to save energy, their usefulness is limited when they are not properly integrated into the daily activities of humans. Energy-saving applications in such environments can benefit from the use of sensors and actuators when data are properly fused with previous knowledge about user habits and feedback about current user preferences. In this article, we present an online recommender system implemented in the EM3 platform, a platform for Consumer Engagement Toward Energy-Saving Behavior. The recommender system uniquely fuses sensors' data with user habits and user feedback and provides personalized recommendations for energy efficiency at the right moment. The user response to the recommendations directly triggers actuators that perform energy-saving actions and is recorded and processed for refining future recommendations. The EM3 recommendation engine continuously evaluates the three inputs (i.e. sensor data, user habits, user feedback) and identifies the micro-moments that maximize the need for the recommended action and thus the recommendation acceptance. We evaluate the efficiency of the proposed recommender system, which is based on a stacked-LSTM for fusing multi-sensor data streams, in several scenarios, and the observed accuracy on predicting the right moment to send a recommendation to the user ranged from 93% to 97%. 2021 Elsevier Ltd
SponsorThis 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.
SubjectData fusion
Energy efficiency
Fusion-based recommendations
Internet of things
Recommender systems
TitleSmart fusion of sensor data and human feedback for personalized energy-saving recommendations
Volume Number305
dc.accessType Abstract Only

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