A Modular Recommender System for Domestic Energy Efficiency
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Alsalemi, Abdullah | |
| dc.contributor.author | Amira, Abbes | |
| dc.contributor.author | Malekmohamadi, Hossein | |
| dc.contributor.author | Diao, Kegong | |
| dc.date.acceptance | 2023-06-15 | |
| dc.date.accessioned | 2023-06-16T13:11:26Z | |
| dc.date.available | 2023-06-16T13:11:26Z | |
| dc.date.issued | 2023-06-15 | |
| dc.description | open access article | |
| dc.description.abstract | 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. | en |
| dc.funder | No external funder | en |
| dc.identifier.citation | Alsalemi, A., Amira, A., Malekmohamadi, H. and Diao, K. (2023) A Modular Recommender System for Domestic Energy Efficiency. Environmental Challenges, 13, 100741 | en |
| dc.identifier.doi | https://doi.org/10.1016/j.envc.2023.100741 | |
| dc.identifier.issn | 2667-0100 | |
| dc.identifier.uri | https://hdl.handle.net/2086/22998 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.publisher | Elsevier | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Energy efficiency | en |
| dc.subject | Sustainability | en |
| dc.subject | Recommender systems | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject | Modularity | en |
| dc.title | A Modular Recommender System for Domestic Energy Efficiency | en |
| dc.type | Article | en |