A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks
In this work, we present a micro-service architecture which defines a Digital Twin (DT) framework for adaptive building automation and control. The DT framework primarily involves the orchestration of several containerized micro-services, promoting the scalability and deployability of the proposed f...
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Format: | Article |
Language: | English |
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EDP Sciences
2022-01-01
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Series: | E3S Web of Conferences |
Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/29/e3sconf_bsn2022_13001.pdf |
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author | Maree Johannes P. Bagle Marius |
author_facet | Maree Johannes P. Bagle Marius |
author_sort | Maree Johannes P. |
collection | DOAJ |
description | In this work, we present a micro-service architecture which defines a Digital Twin (DT) framework for adaptive building automation and control. The DT framework primarily involves the orchestration of several containerized micro-services, promoting the scalability and deployability of the proposed framework within the industrial context. In the proposed framework, containerized microservices facilitate: (i) model-based control strategies; (ii) data-driven learning; (iii) data management; (iv) the inclusion of an internal High-Fidelity Simulator (HFS) to enable bootstrapped learning; and (v) a User Interface/User Experience (UI/UE) micro-service orchestrator. To validate the usefulness of the proposed framework, we implement a Physics Inspired Neural Network (PINN) to adapt the model-based control strategies for plant-model uncertainty and utilize bootstrap sampling against an internal HFS. |
first_indexed | 2024-04-11T13:43:43Z |
format | Article |
id | doaj.art-158f13fbba6f4d6eb950a390b6f8cc46 |
institution | Directory Open Access Journal |
issn | 2267-1242 |
language | English |
last_indexed | 2024-04-11T13:43:43Z |
publishDate | 2022-01-01 |
publisher | EDP Sciences |
record_format | Article |
series | E3S Web of Conferences |
spelling | doaj.art-158f13fbba6f4d6eb950a390b6f8cc462022-12-22T04:21:10ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013621300110.1051/e3sconf/202236213001e3sconf_bsn2022_13001A Building Automation and Control micro-service architecture using Physics Inspired Neural NetworksMaree Johannes P.0Bagle Marius1SINTEF DigitalSINTEF CommunityIn this work, we present a micro-service architecture which defines a Digital Twin (DT) framework for adaptive building automation and control. The DT framework primarily involves the orchestration of several containerized micro-services, promoting the scalability and deployability of the proposed framework within the industrial context. In the proposed framework, containerized microservices facilitate: (i) model-based control strategies; (ii) data-driven learning; (iii) data management; (iv) the inclusion of an internal High-Fidelity Simulator (HFS) to enable bootstrapped learning; and (v) a User Interface/User Experience (UI/UE) micro-service orchestrator. To validate the usefulness of the proposed framework, we implement a Physics Inspired Neural Network (PINN) to adapt the model-based control strategies for plant-model uncertainty and utilize bootstrap sampling against an internal HFS.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/29/e3sconf_bsn2022_13001.pdf |
spellingShingle | Maree Johannes P. Bagle Marius A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks E3S Web of Conferences |
title | A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks |
title_full | A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks |
title_fullStr | A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks |
title_full_unstemmed | A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks |
title_short | A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks |
title_sort | building automation and control micro service architecture using physics inspired neural networks |
url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/29/e3sconf_bsn2022_13001.pdf |
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