A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks
Peer reviewed, Journal article
Published version
![Thumbnail](/ntnu-xmlui/bitstream/handle/11250/3050297/e3sconf_bsn2022_13001.pdf.jpg?sequence=6&isAllowed=y)
View/ Open
Date
2022Metadata
Show full item recordCollections
Original version
10.1051/e3sconf/202236213001Abstract
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.