A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
المؤلف | Himeur, Yassine |
المؤلف | Alsalemi, Abdullah |
المؤلف | Al-Kababji, Ayman |
المؤلف | Bensaali, Faycal |
المؤلف | Amira, Abbes |
المؤلف | Sardianos, Christos |
المؤلف | Dimitrakopoulos, George |
المؤلف | Varlamis, Iraklis |
تاريخ الإتاحة | 2022-12-29T07:34:45Z |
تاريخ النشر | 2021 |
اسم المنشور | Information Fusion |
المصدر | Scopus |
الملخص | Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions. 2021 The Authors |
راعي المشروع | 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. All authors approved the version of the manuscript to be published. Open Access funding provided by the Qatar National Library. |
اللغة | en |
الناشر | Elsevier |
الموضوع | Artificial intelligence Energy efficiency Evaluation metrics Explainable recommender systems Recommender systems Visualization |
النوع | Short Survey |
الصفحات | 1-21 |
رقم المجلد | 72 |
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