Real-time personalised energy saving recommendations
Author | Sardianos, Christos |
Author | Chronis, Christos |
Author | Varlamis, Iraklis |
Author | Dimitrakopoulos, George |
Author | Himeur, Yassine |
Author | Alsalemi, Abdullah |
Author | Bensaali, Faycal |
Author | Amira, Abbes |
Available date | 2022-12-29T07:34:46Z |
Publication Date | 2020 |
Publication Name | Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020 |
Resource | Scopus |
Abstract | The increased consumption of energy worldwide has boosted the interest of people for energy-efficient solutions at every level of daily life, from goods production and transportation to the use of household and office appliances. This gave rise to monitoring applications that monitor the daily user interaction with the electrical and electronic appliances, detect unnecessary or extensive usage and recommend corrective actions. In this direction, this work presents the anatomy of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 recommendation engine, which supports household and office users with real-time personalized recommendations for avoiding unnecessary energy consumption and reducing the overall household (or office) energy footprint. The recommendation engine is based on a set of sensors that monitor energy usage, room occupancy, and environmental conditions inside and outside the living space, and a set of actuators that allow the remote control of devices, (e.g. on and off actions, set to eco or standby mode, etc.). The innovating feature of this recommendation engine is that it puts the human in the loop of energy efficiency by recommending actions at the right moment, in real-time, with user approval and rejection options. In addition, it provides savings related facts in order to increase the persuasiveness of the recommendations. Initial results show that users respond positively to personalized recommendations and are further persuaded when specific types of facts are chosen. 2020 IEEE. |
Sponsor | ACKNOWLEDGMENT 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. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | energy efficiency personalised persuasiveness real-time recommendations recommender systems |
Type | Conference Paper |
Pagination | 366-371 |
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Electrical Engineering [2611 items ]