A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
<p dir="ltr">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 incorporat...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2021
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513559595057152 |
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| author | Yassine Himeur (14158821) |
| author2 | Abdullah Alsalemi (6951986) Ayman Al-Kababji (16870080) Faycal Bensaali (12427401) Abbes Amira (6952001) Christos Sardianos (8297297) George Dimitrakopoulos (16855419) Iraklis Varlamis (9288743) |
| author2_role | author author author author author author author |
| author_facet | Yassine Himeur (14158821) Abdullah Alsalemi (6951986) Ayman Al-Kababji (16870080) Faycal Bensaali (12427401) Abbes Amira (6952001) Christos Sardianos (8297297) George Dimitrakopoulos (16855419) Iraklis Varlamis (9288743) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yassine Himeur (14158821) Abdullah Alsalemi (6951986) Ayman Al-Kababji (16870080) Faycal Bensaali (12427401) Abbes Amira (6952001) Christos Sardianos (8297297) George Dimitrakopoulos (16855419) Iraklis Varlamis (9288743) |
| dc.date.none.fl_str_mv | 2021-08-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.inffus.2021.02.002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_survey_of_recommender_systems_for_energy_efficiency_in_buildings_Principles_challenges_and_prospects/24083091 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Built environment and design Building Engineering Electrical engineering Information and computing sciences Artificial intelligence Recommender systems Energy efficiency Evaluation metrics Artificial intelligence Explainable recommender systems Visualization |
| dc.title.none.fl_str_mv | A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Information Fusion<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.inffus.2021.02.002" target="_blank">https://dx.doi.org/10.1016/j.inffus.2021.02.002</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_157153fe0082a2dc8aa016ccfb3cea6d |
| identifier_str_mv | 10.1016/j.inffus.2021.02.002 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24083091 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospectsYassine Himeur (14158821)Abdullah Alsalemi (6951986)Ayman Al-Kababji (16870080)Faycal Bensaali (12427401)Abbes Amira (6952001)Christos Sardianos (8297297)George Dimitrakopoulos (16855419)Iraklis Varlamis (9288743)Built environment and designBuildingEngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceRecommender systemsEnergy efficiencyEvaluation metricsArtificial intelligenceExplainable recommender systemsVisualization<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Information Fusion<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.inffus.2021.02.002" target="_blank">https://dx.doi.org/10.1016/j.inffus.2021.02.002</a></p>2021-08-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.inffus.2021.02.002https://figshare.com/articles/journal_contribution/A_survey_of_recommender_systems_for_energy_efficiency_in_buildings_Principles_challenges_and_prospects/24083091CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240830912021-08-01T00:00:00Z |
| spellingShingle | A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects Yassine Himeur (14158821) Built environment and design Building Engineering Electrical engineering Information and computing sciences Artificial intelligence Recommender systems Energy efficiency Evaluation metrics Artificial intelligence Explainable recommender systems Visualization |
| status_str | publishedVersion |
| title | A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects |
| title_full | A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects |
| title_fullStr | A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects |
| title_full_unstemmed | A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects |
| title_short | A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects |
| title_sort | A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects |
| topic | Built environment and design Building Engineering Electrical engineering Information and computing sciences Artificial intelligence Recommender systems Energy efficiency Evaluation metrics Artificial intelligence Explainable recommender systems Visualization |