Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond
<p dir="ltr">Occupancy detection is crucial for various applications, including smart buildings, security systems, and energy management. This paper introduces a novel convolutional neural network (CNN) architecture based on an image encoding approach for accurate occupancy detection...
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2025
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| _version_ | 1864513533217079296 |
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| author | Aya Nabil Sayed (17317006) |
| author2 | Sakib Mahmud (15302404) Faycal Bensaali (12427401) Muhammad E. H. Chowdhury (14150526) Yassine Himeur (14158821) |
| author2_role | author author author author |
| author_facet | Aya Nabil Sayed (17317006) Sakib Mahmud (15302404) Faycal Bensaali (12427401) Muhammad E. H. Chowdhury (14150526) Yassine Himeur (14158821) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aya Nabil Sayed (17317006) Sakib Mahmud (15302404) Faycal Bensaali (12427401) Muhammad E. H. Chowdhury (14150526) Yassine Himeur (14158821) |
| dc.date.none.fl_str_mv | 2025-06-03T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-025-11348-6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Optimizing_energy_efficiency_through_precise_occupancy_detection_A_tailored_CNN_architecture_for_smart_buildings_and_beyond/30540707 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Smart buildings Occupancy detection Feature extraction Image encoding Convolutional neural network |
| dc.title.none.fl_str_mv | Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Occupancy detection is crucial for various applications, including smart buildings, security systems, and energy management. This paper introduces a novel convolutional neural network (CNN) architecture based on an image encoding approach for accurate occupancy detection. Our network effectively extracts relevant features from occupancy images by leveraging deep learning and image processing techniques, enabling reliable and real-time detection. We employed an image encoding method that converts environmental time-series data into 2D image representations—either grayscale or RGB-like—depending on the input requirements of the CNN model. This transformation captures spatial and temporal characteristics of the data, allowing the network to learn more expressive occupancy-related patterns from raw 1D input. Additionally, we developed a custom CNN architecture optimized for the encoded images, enabling the network to identify key features and understand complex spatial relationships. We evaluated the performance of our CNN through extensive testing on well-known occupancy datasets. The results highlight the superiority of our approach, outperforming existing techniques in accuracy, precision, recall, and F1-score. Our model achieved impressive accuracies of 98.45%, 99.05%, and 97.32% across the three datasets used in this study.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-025-11348-6" target="_blank">https://dx.doi.org/10.1007/s00521-025-11348-6</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_50c26bf087b03ed562f48ef779ffb0b1 |
| identifier_str_mv | 10.1007/s00521-025-11348-6 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30540707 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyondAya Nabil Sayed (17317006)Sakib Mahmud (15302404)Faycal Bensaali (12427401)Muhammad E. H. Chowdhury (14150526)Yassine Himeur (14158821)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningSmart buildingsOccupancy detectionFeature extractionImage encodingConvolutional neural network<p dir="ltr">Occupancy detection is crucial for various applications, including smart buildings, security systems, and energy management. This paper introduces a novel convolutional neural network (CNN) architecture based on an image encoding approach for accurate occupancy detection. Our network effectively extracts relevant features from occupancy images by leveraging deep learning and image processing techniques, enabling reliable and real-time detection. We employed an image encoding method that converts environmental time-series data into 2D image representations—either grayscale or RGB-like—depending on the input requirements of the CNN model. This transformation captures spatial and temporal characteristics of the data, allowing the network to learn more expressive occupancy-related patterns from raw 1D input. Additionally, we developed a custom CNN architecture optimized for the encoded images, enabling the network to identify key features and understand complex spatial relationships. We evaluated the performance of our CNN through extensive testing on well-known occupancy datasets. The results highlight the superiority of our approach, outperforming existing techniques in accuracy, precision, recall, and F1-score. Our model achieved impressive accuracies of 98.45%, 99.05%, and 97.32% across the three datasets used in this study.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-025-11348-6" target="_blank">https://dx.doi.org/10.1007/s00521-025-11348-6</a></p>2025-06-03T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-025-11348-6https://figshare.com/articles/journal_contribution/Optimizing_energy_efficiency_through_precise_occupancy_detection_A_tailored_CNN_architecture_for_smart_buildings_and_beyond/30540707CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305407072025-06-03T03:00:00Z |
| spellingShingle | Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond Aya Nabil Sayed (17317006) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Smart buildings Occupancy detection Feature extraction Image encoding Convolutional neural network |
| status_str | publishedVersion |
| title | Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond |
| title_full | Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond |
| title_fullStr | Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond |
| title_full_unstemmed | Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond |
| title_short | Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond |
| title_sort | Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Smart buildings Occupancy detection Feature extraction Image encoding Convolutional neural network |