Deep and transfer learning for building occupancy detection: A review and comparative analysis
<p dir="ltr">The building internet of things (BIoT) is quite a promising concept for curtailing energy consumption, reducing costs, and promoting building transformation. Besides, integrating artificial intelligence (AI) into the BIoT is essential for data analysis and intelligent de...
محفوظ في:
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| مؤلفون آخرون: | , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513536538968064 |
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| author | Aya Nabil Sayed (17317006) |
| author2 | Yassine Himeur (14158821) Faycal Bensaali (12427401) |
| author2_role | author author |
| author_facet | Aya Nabil Sayed (17317006) Yassine Himeur (14158821) Faycal Bensaali (12427401) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aya Nabil Sayed (17317006) Yassine Himeur (14158821) Faycal Bensaali (12427401) |
| dc.date.none.fl_str_mv | 2022-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.engappai.2022.105254 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_and_transfer_learning_for_building_occupancy_detection_A_review_and_comparative_analysis/24720249 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Occupancy detection Non-intrusive Internet of energy Energy efficiency Edge devices Big data Deep learning Transfer learning |
| dc.title.none.fl_str_mv | Deep and transfer learning for building occupancy detection: A review and comparative analysis |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The building internet of things (BIoT) is quite a promising concept for curtailing energy consumption, reducing costs, and promoting building transformation. Besides, integrating artificial intelligence (AI) into the BIoT is essential for data analysis and intelligent decision-making. Thus, data-driven approaches to infer occupancy patterns usage are gaining growing interest in BIoT applications. Typically, analyzing big occupancy data gathered by BIoT networks helps significantly identify the causes of wasted energy and recommend corrective actions. Within this context, building occupancy data aids in the improvement of the efficacy of energy management systems, allowing the reduction of energy consumption while maintaining occupant comfort. Occupancy data might be collected using a variety of devices. Among those devices are optical/thermal cameras, smart meters, environmental sensors such as carbon dioxide (CO<sub>2</sub>), and passive infrared (PIR). Even though the latter methods are less precise, they have generated considerable attention owing to their inexpensive cost and low invasive nature. This article provides an in-depth survey of the strategies used to analyze sensor data and determine occupancy. The article’s primary emphasis is on reviewing deep learning (DL), and transfer learning (TL) approaches for occupancy detection. This work investigates occupancy detection methods to develop an efficient system for processing sensor data while providing accurate occupancy information. Moreover, the paper conducted a comparative study of the readily available algorithms for occupancy detection to determine the optimal method in regards to training time and testing accuracy. The main concerns affecting the current occupancy detection system in terms of privacy and precision were thoroughly discussed. For occupancy detection, several directions were provided to avoid or reduce privacy problems by employing forthcoming technologies such as edge devices, Federated learning, and Blockchain-based IoT.</p><h2>Other Information</h2><p dir="ltr">Published in: Engineering Applications of Artificial Intelligence<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.engappai.2022.105254" target="_blank">https://dx.doi.org/10.1016/j.engappai.2022.105254</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_afd6a96652263dadc34a016ecadb07d3 |
| identifier_str_mv | 10.1016/j.engappai.2022.105254 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24720249 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep and transfer learning for building occupancy detection: A review and comparative analysisAya Nabil Sayed (17317006)Yassine Himeur (14158821)Faycal Bensaali (12427401)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceDistributed computing and systems softwareMachine learningOccupancy detectionNon-intrusiveInternet of energyEnergy efficiencyEdge devicesBig dataDeep learningTransfer learning<p dir="ltr">The building internet of things (BIoT) is quite a promising concept for curtailing energy consumption, reducing costs, and promoting building transformation. Besides, integrating artificial intelligence (AI) into the BIoT is essential for data analysis and intelligent decision-making. Thus, data-driven approaches to infer occupancy patterns usage are gaining growing interest in BIoT applications. Typically, analyzing big occupancy data gathered by BIoT networks helps significantly identify the causes of wasted energy and recommend corrective actions. Within this context, building occupancy data aids in the improvement of the efficacy of energy management systems, allowing the reduction of energy consumption while maintaining occupant comfort. Occupancy data might be collected using a variety of devices. Among those devices are optical/thermal cameras, smart meters, environmental sensors such as carbon dioxide (CO<sub>2</sub>), and passive infrared (PIR). Even though the latter methods are less precise, they have generated considerable attention owing to their inexpensive cost and low invasive nature. This article provides an in-depth survey of the strategies used to analyze sensor data and determine occupancy. The article’s primary emphasis is on reviewing deep learning (DL), and transfer learning (TL) approaches for occupancy detection. This work investigates occupancy detection methods to develop an efficient system for processing sensor data while providing accurate occupancy information. Moreover, the paper conducted a comparative study of the readily available algorithms for occupancy detection to determine the optimal method in regards to training time and testing accuracy. The main concerns affecting the current occupancy detection system in terms of privacy and precision were thoroughly discussed. For occupancy detection, several directions were provided to avoid or reduce privacy problems by employing forthcoming technologies such as edge devices, Federated learning, and Blockchain-based IoT.</p><h2>Other Information</h2><p dir="ltr">Published in: Engineering Applications of Artificial Intelligence<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.engappai.2022.105254" target="_blank">https://dx.doi.org/10.1016/j.engappai.2022.105254</a></p>2022-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.engappai.2022.105254https://figshare.com/articles/journal_contribution/Deep_and_transfer_learning_for_building_occupancy_detection_A_review_and_comparative_analysis/24720249CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247202492022-10-01T00:00:00Z |
| spellingShingle | Deep and transfer learning for building occupancy detection: A review and comparative analysis Aya Nabil Sayed (17317006) Engineering Electrical engineering Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Occupancy detection Non-intrusive Internet of energy Energy efficiency Edge devices Big data Deep learning Transfer learning |
| status_str | publishedVersion |
| title | Deep and transfer learning for building occupancy detection: A review and comparative analysis |
| title_full | Deep and transfer learning for building occupancy detection: A review and comparative analysis |
| title_fullStr | Deep and transfer learning for building occupancy detection: A review and comparative analysis |
| title_full_unstemmed | Deep and transfer learning for building occupancy detection: A review and comparative analysis |
| title_short | Deep and transfer learning for building occupancy detection: A review and comparative analysis |
| title_sort | Deep and transfer learning for building occupancy detection: A review and comparative analysis |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Occupancy detection Non-intrusive Internet of energy Energy efficiency Edge devices Big data Deep learning Transfer learning |