Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort
<p dir="ltr">Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these B...
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2023
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| _version_ | 1864513507705225216 |
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| author | Sergio Márquez-Sánchez (19437985) |
| author2 | Jaime Calvo-Gallego (10968849) Aiman Erbad (14150589) Muhammad Ibrar (9732177) Javier Hernandez Fernandez (19418752) Mahdi Houchati (16891560) Juan Manuel Corchado (12582511) |
| author2_role | author author author author author author |
| author_facet | Sergio Márquez-Sánchez (19437985) Jaime Calvo-Gallego (10968849) Aiman Erbad (14150589) Muhammad Ibrar (9732177) Javier Hernandez Fernandez (19418752) Mahdi Houchati (16891560) Juan Manuel Corchado (12582511) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sergio Márquez-Sánchez (19437985) Jaime Calvo-Gallego (10968849) Aiman Erbad (14150589) Muhammad Ibrar (9732177) Javier Hernandez Fernandez (19418752) Mahdi Houchati (16891560) Juan Manuel Corchado (12582511) |
| dc.date.none.fl_str_mv | 2023-10-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/electronics12194179 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Enhancing_Building_Energy_Management_Adaptive_Edge_Computing_for_Optimized_Efficiency_and_Inhabitant_Comfort/26772151 |
| 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 Communications engineering Information and computing sciences Machine learning building and energy management systems (BEMSs) edge computing energy efficiency (EE) federated learning (FL) deep reinforcement learning (deep RL) internet of things (IoT) virtual organizations |
| dc.title.none.fl_str_mv | Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. In response to these challenges, this article introduces a cutting-edge edge computing architecture grounded in virtual organizations, federated learning, and deep reinforcement learning algorithms, tailored to optimize energy consumption within buildings/homes and facilitate demand response. By integrating energy efficiency measures within virtual organizations, which dynamically learn from real-time inhabitant data while prioritizing comfort, our approach effectively optimizes inhabitant consumption patterns, ushering in a new era of energy efficiency in the built environment.</p><h2>Other Information</h2><p dir="ltr">Published in: Electronics<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/10.3390/electronics12194179" target="_blank">https://dx.doi.org/10.3390/electronics12194179</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a2f9fa25b586fc71e3d0aa9986811145 |
| identifier_str_mv | 10.3390/electronics12194179 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26772151 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant ComfortSergio Márquez-Sánchez (19437985)Jaime Calvo-Gallego (10968849)Aiman Erbad (14150589)Muhammad Ibrar (9732177)Javier Hernandez Fernandez (19418752)Mahdi Houchati (16891560)Juan Manuel Corchado (12582511)Built environment and designBuildingEngineeringCommunications engineeringInformation and computing sciencesMachine learningbuilding and energy management systems (BEMSs)edge computingenergy efficiency (EE)federated learning (FL)deep reinforcement learning (deep RL)internet of things (IoT)virtual organizations<p dir="ltr">Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. In response to these challenges, this article introduces a cutting-edge edge computing architecture grounded in virtual organizations, federated learning, and deep reinforcement learning algorithms, tailored to optimize energy consumption within buildings/homes and facilitate demand response. By integrating energy efficiency measures within virtual organizations, which dynamically learn from real-time inhabitant data while prioritizing comfort, our approach effectively optimizes inhabitant consumption patterns, ushering in a new era of energy efficiency in the built environment.</p><h2>Other Information</h2><p dir="ltr">Published in: Electronics<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/10.3390/electronics12194179" target="_blank">https://dx.doi.org/10.3390/electronics12194179</a></p>2023-10-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/electronics12194179https://figshare.com/articles/journal_contribution/Enhancing_Building_Energy_Management_Adaptive_Edge_Computing_for_Optimized_Efficiency_and_Inhabitant_Comfort/26772151CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267721512023-10-09T03:00:00Z |
| spellingShingle | Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort Sergio Márquez-Sánchez (19437985) Built environment and design Building Engineering Communications engineering Information and computing sciences Machine learning building and energy management systems (BEMSs) edge computing energy efficiency (EE) federated learning (FL) deep reinforcement learning (deep RL) internet of things (IoT) virtual organizations |
| status_str | publishedVersion |
| title | Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort |
| title_full | Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort |
| title_fullStr | Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort |
| title_full_unstemmed | Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort |
| title_short | Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort |
| title_sort | Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort |
| topic | Built environment and design Building Engineering Communications engineering Information and computing sciences Machine learning building and energy management systems (BEMSs) edge computing energy efficiency (EE) federated learning (FL) deep reinforcement learning (deep RL) internet of things (IoT) virtual organizations |