Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring
<p dir="ltr">The unprecedented rise in global temperatures, extreme weather events, and the depletion of <u>natural resources</u> underscore the urgent need for <u>sustainable practices</u>. Energy-saving solutions are pivotal in mitigating these challenges by...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513539396337664 |
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| author | Aya Nabil Sayed (17317006) |
| author2 | Faycal Bensaali (12427401) Yassine Himeur (14158821) George Dimitrakopoulos (16855419) Iraklis Varlamis (9288743) |
| author2_role | author author author author |
| author_facet | Aya Nabil Sayed (17317006) Faycal Bensaali (12427401) Yassine Himeur (14158821) George Dimitrakopoulos (16855419) Iraklis Varlamis (9288743) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aya Nabil Sayed (17317006) Faycal Bensaali (12427401) Yassine Himeur (14158821) George Dimitrakopoulos (16855419) Iraklis Varlamis (9288743) |
| dc.date.none.fl_str_mv | 2024-12-17T18:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.enbuild.2024.115151 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Enhancing_building_sustainability_A_Digital_Twin_approach_to_energy_efficiency_and_occupancy_monitoring/30173458 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Environmental engineering Information and computing sciences Machine learning Digital Twins Internet of things Recommender system Occupancy detection Energy efficiency Home-assistant |
| dc.title.none.fl_str_mv | Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The unprecedented rise in global temperatures, extreme weather events, and the depletion of <u>natural resources</u> underscore the urgent need for <u>sustainable practices</u>. Energy-saving solutions are pivotal in mitigating these challenges by reducing carbon emissions, lessening our reliance on finite energy sources, and ultimately contributing to a more resilient and environmentally conscious future. Thus, this paper presents a novel approach to enhancing energy efficiency within residential environments by integrating a <u>Digital Twin</u> (DT) on the Home-Assistant platform. Home-Assistant provides a user-centric approach to the <u>DT technology</u>, allowing homeowners to establish and oversee virtual replicas of their living spaces by incorporating a variety of<u> Internet of Things</u> (IoT) devices and sensors. Leveraging the capabilities of this open-source home automation platform, we have developed a sophisticated system for providing real-time <u>energy consumption data</u>, personalized energy-saving recommendations, and a data-driven occupancy detection mechanism. This system was rigorously tested in a controlled laboratory environment with multiple users, simulating diverse household scenarios. The DT technology enabled the creation of accurate virtual representations of users' physical environment, facilitating the optimization of energy-intensive devices and systems. Our data-driven occupancy detection approach utilized Machine Learning (ML) algorithms to intelligently determine room occupancy, allowing for precise energy management based on real-time usage patterns. The occupancy detection approach has proven highly effective, with a testing accuracy rate of 95.12% and f1-scores of 94.55%. Additionally, a mini-pilot study evaluated the <u>recommender system</u> and found an outstanding 80% favorable user reaction, demonstrating its efficiency in giving energy-saving advice. Moreover, the DT comprehensively represented the user's state of presence, their engagement with the connected appliances, and the ambient conditions of the lab. These results demonstrate significant potential for reducing energy waste and cost while maintaining user comfort. This research contributes to the growing field of <u>smart home</u> technology by showcasing the practical implementation of DT and data-driven strategies to promote sustainable and efficient energy practices in everyday living spaces.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy and Buildings<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.enbuild.2024.115151" target="_blank">https://dx.doi.org/10.1016/j.enbuild.2024.115151</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_91649fe2097923361e12eb49dd5f9cdf |
| identifier_str_mv | 10.1016/j.enbuild.2024.115151 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30173458 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoringAya Nabil Sayed (17317006)Faycal Bensaali (12427401)Yassine Himeur (14158821)George Dimitrakopoulos (16855419)Iraklis Varlamis (9288743)EngineeringEnvironmental engineeringInformation and computing sciencesMachine learningDigital TwinsInternet of thingsRecommender systemOccupancy detectionEnergy efficiencyHome-assistant<p dir="ltr">The unprecedented rise in global temperatures, extreme weather events, and the depletion of <u>natural resources</u> underscore the urgent need for <u>sustainable practices</u>. Energy-saving solutions are pivotal in mitigating these challenges by reducing carbon emissions, lessening our reliance on finite energy sources, and ultimately contributing to a more resilient and environmentally conscious future. Thus, this paper presents a novel approach to enhancing energy efficiency within residential environments by integrating a <u>Digital Twin</u> (DT) on the Home-Assistant platform. Home-Assistant provides a user-centric approach to the <u>DT technology</u>, allowing homeowners to establish and oversee virtual replicas of their living spaces by incorporating a variety of<u> Internet of Things</u> (IoT) devices and sensors. Leveraging the capabilities of this open-source home automation platform, we have developed a sophisticated system for providing real-time <u>energy consumption data</u>, personalized energy-saving recommendations, and a data-driven occupancy detection mechanism. This system was rigorously tested in a controlled laboratory environment with multiple users, simulating diverse household scenarios. The DT technology enabled the creation of accurate virtual representations of users' physical environment, facilitating the optimization of energy-intensive devices and systems. Our data-driven occupancy detection approach utilized Machine Learning (ML) algorithms to intelligently determine room occupancy, allowing for precise energy management based on real-time usage patterns. The occupancy detection approach has proven highly effective, with a testing accuracy rate of 95.12% and f1-scores of 94.55%. Additionally, a mini-pilot study evaluated the <u>recommender system</u> and found an outstanding 80% favorable user reaction, demonstrating its efficiency in giving energy-saving advice. Moreover, the DT comprehensively represented the user's state of presence, their engagement with the connected appliances, and the ambient conditions of the lab. These results demonstrate significant potential for reducing energy waste and cost while maintaining user comfort. This research contributes to the growing field of <u>smart home</u> technology by showcasing the practical implementation of DT and data-driven strategies to promote sustainable and efficient energy practices in everyday living spaces.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy and Buildings<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.enbuild.2024.115151" target="_blank">https://dx.doi.org/10.1016/j.enbuild.2024.115151</a></p>2024-12-17T18:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.enbuild.2024.115151https://figshare.com/articles/journal_contribution/Enhancing_building_sustainability_A_Digital_Twin_approach_to_energy_efficiency_and_occupancy_monitoring/30173458CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301734582024-12-17T18:00:00Z |
| spellingShingle | Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring Aya Nabil Sayed (17317006) Engineering Environmental engineering Information and computing sciences Machine learning Digital Twins Internet of things Recommender system Occupancy detection Energy efficiency Home-assistant |
| status_str | publishedVersion |
| title | Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring |
| title_full | Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring |
| title_fullStr | Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring |
| title_full_unstemmed | Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring |
| title_short | Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring |
| title_sort | Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring |
| topic | Engineering Environmental engineering Information and computing sciences Machine learning Digital Twins Internet of things Recommender system Occupancy detection Energy efficiency Home-assistant |