Authors:
Christos Sardianos
1
;
Iraklis Varlamis
1
;
George Dimitrakopoulos
1
;
Dimosthenis Anagnostopoulos
1
;
Abdullah Alsalemi
2
;
Faycal Bensaali
2
and
Abbes Amira
3
Affiliations:
1
Department of Informatics and Telematics, Harokopio University of Athens, Athens and Greece
;
2
Department of Electrical Engineering, Qatar University, Doha and Qatar
;
3
Department of Computer Science and Engineering, Qatar University, Doha and Qatar
Keyword(s):
Recommender Systems, Energy Saving Recommendations, Micro-moments, Energy Habits.
Related
Ontology
Subjects/Areas/Topics:
Energy and Economy
;
Energy-Aware Systems and Technologies
;
Optimization Techniques for Efficient Energy Consumption
;
Smart Cities
;
Smart Grids
;
Smart Homes (Domotics)
;
Smart Sensor-Based Networks and Applications
;
User-Centred and Participatory Design of Services and Systems for Smart Cities
Abstract:
Since electricity consumption of households in developing countries is dramatically increasing every year, it is now more prudent than ever to utilize technology-based solutions that assist energy end-users to improve energy efficiency without affecting quality of life. User behavior is the most important factor that influences household energy consumption and recommender systems can be the technology enabler for shaping the users’ behavior towards energy efficiency. The current literature mostly focuses on energy usage monitoring and home automation and fails to engage and motivate users, who are not as committed and self-motivated. In this work, we present a context-aware recommender system that analyses user activities and understands their habits. Based on the output of this analysis, the system synchronizes with the user activities and presents personalized energy efficiency recommendations at the right moment and place. The recommendation algorithm considers user preferences, e
nergy goals, and availability in order to maximize the acceptance of a recommended action and increase the efficiency of the recommender system. The results from the evaluation on a publicly available dataset comprising energy consumption data from multiple devices shows that micro-moments repeatedly occur within user’s timeline (covering more than 35% of user future activities) and can be learned from user logs.
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