A modular recommender system for domestic energy efficiency

• Published in: Environmental challenges (Amsterdam, Netherlands) Vol. 13; p. 100741
• Main Authors: Alsalemi, Abdullah, Amira, Abbes, Malekmohamadi, Hossein, Diao, Kegong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2023; Elsevier
• Subjects: Artificial intelligence; Energy efficiency; Modularity; Recommender systems; Sustainability; Energy efficiency; Artificial intelligence; Modularity; Sustainability; Recommender systems
• ISSN: 2667-0100; 2667-0100
• DOI: 10.1016/j.envc.2023.100741

Recommender systems continually impact multiple verticals by introducing automated intelligence to decision making. When applying such Artificial Intelligence (AI) tools to energy efficiency problems, a number of opportunities and challenges present themselves. This paper presents a modular recommender system for improving domestic household energy savings. The recommender relies upon a contextual appliance-level energy dataset from seven appliances in a household. Modularity is incorporated into the system design to create customizable sub-components that adapt to the nature of the data and the end-user's preference, such as modules that recommend based on usage patterns, power consumption, and occupancy. Machine Learning (ML) has been used for automatic appliance profiling and rank-based methods are employed to evaluate the recommender based on relevance scores. Implementation results for generating recommendations for two weeks yield a Root Mean Square Error (RMSE) of 0.2288, Normalized Cumulative Discounted Gain (NCDG) of 0.729 for seven appliances. Future work includes evaluation on edge computing platforms and user testing through a mobile application.