Development of a Cognitive Digital Twin for Building Management and Operations
Cognitive Digital Twins (CDTs) are defined as capable of achieving some elements of cognition, notably memory (encoding and retrieval), perception (creating useful data representations), and reasoning (outlier and event detection). This paper presents the development of a CDT, populated by construct...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Built Environment |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbuil.2022.856873/full |
| _version_ | 1818214670409924608 |
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| author | Karim El Mokhtari Karim El Mokhtari Ivan Panushev J. J. McArthur |
| author_facet | Karim El Mokhtari Karim El Mokhtari Ivan Panushev J. J. McArthur |
| author_sort | Karim El Mokhtari |
| collection | DOAJ |
| description | Cognitive Digital Twins (CDTs) are defined as capable of achieving some elements of cognition, notably memory (encoding and retrieval), perception (creating useful data representations), and reasoning (outlier and event detection). This paper presents the development of a CDT, populated by construction information, facility management data, and data streamed from the Building Automation System (BAS). Advanced machine learning was enabled by access to both real-time and historical data coupled with scalable cloud-based computational resources. Streaming data to the cloud has been implemented in existing architectures; to address security concerns from exposing building equipment to undesirable access, a secure streaming architecture from BACnet equipment to our research cloud is presented. Real-time data is uploaded to a high-performance scalable time-series database, while the ontology is stored on a relational database. Both data sources are integrated with Building Information Models (BIM) to aggregate, explore, and visualize information on demand. This paper presents a case study of a Digital Twin (DT) of an academic building where various capabilities of CDTs are demonstrated through a series of proof-of-concept examples. Drawing from our experience enhancing this implementation with elements of cognition, we present a development framework and reference architecture to guide future whole-building CDT research. |
| first_indexed | 2024-12-12T06:23:52Z |
| format | Article |
| id | doaj.art-135fbbdd9b944060b2764b027facb82e |
| institution | Directory Open Access Journal |
| issn | 2297-3362 |
| language | English |
| last_indexed | 2024-12-12T06:23:52Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Built Environment |
| spelling | doaj.art-135fbbdd9b944060b2764b027facb82e2022-12-22T00:34:49ZengFrontiers Media S.A.Frontiers in Built Environment2297-33622022-05-01810.3389/fbuil.2022.856873856873Development of a Cognitive Digital Twin for Building Management and OperationsKarim El Mokhtari0Karim El Mokhtari1Ivan Panushev2J. J. McArthur3Smart Building Research Group, Ryerson University, Department Architectural Science, Toronto, ON, CanadaFuseForward, Vancouver, BC, CanadaEngineering, Construction & Real Estate, AWS, New York, NY, United StatesSmart Building Research Group, Ryerson University, Department Architectural Science, Toronto, ON, CanadaCognitive Digital Twins (CDTs) are defined as capable of achieving some elements of cognition, notably memory (encoding and retrieval), perception (creating useful data representations), and reasoning (outlier and event detection). This paper presents the development of a CDT, populated by construction information, facility management data, and data streamed from the Building Automation System (BAS). Advanced machine learning was enabled by access to both real-time and historical data coupled with scalable cloud-based computational resources. Streaming data to the cloud has been implemented in existing architectures; to address security concerns from exposing building equipment to undesirable access, a secure streaming architecture from BACnet equipment to our research cloud is presented. Real-time data is uploaded to a high-performance scalable time-series database, while the ontology is stored on a relational database. Both data sources are integrated with Building Information Models (BIM) to aggregate, explore, and visualize information on demand. This paper presents a case study of a Digital Twin (DT) of an academic building where various capabilities of CDTs are demonstrated through a series of proof-of-concept examples. Drawing from our experience enhancing this implementation with elements of cognition, we present a development framework and reference architecture to guide future whole-building CDT research.https://www.frontiersin.org/articles/10.3389/fbuil.2022.856873/fullcognitive digital twinsmart buildingdata visualizationdata streamingIoT |
| spellingShingle | Karim El Mokhtari Karim El Mokhtari Ivan Panushev J. J. McArthur Development of a Cognitive Digital Twin for Building Management and Operations Frontiers in Built Environment cognitive digital twin smart building data visualization data streaming IoT |
| title | Development of a Cognitive Digital Twin for Building Management and Operations |
| title_full | Development of a Cognitive Digital Twin for Building Management and Operations |
| title_fullStr | Development of a Cognitive Digital Twin for Building Management and Operations |
| title_full_unstemmed | Development of a Cognitive Digital Twin for Building Management and Operations |
| title_short | Development of a Cognitive Digital Twin for Building Management and Operations |
| title_sort | development of a cognitive digital twin for building management and operations |
| topic | cognitive digital twin smart building data visualization data streaming IoT |
| url | https://www.frontiersin.org/articles/10.3389/fbuil.2022.856873/full |
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