Issue |
E3S Web Conf.
Volume 362, 2022
BuildSim Nordic 2022
|
|
---|---|---|
Article Number | 13001 | |
Number of page(s) | 7 | |
Section | Commissioning and Demand Response | |
DOI | https://doi.org/10.1051/e3sconf/202236213001 | |
Published online | 01 December 2022 |
A Building Automation and Control micro-service architecture using Physics Inspired Neural Networks
1 SINTEF Digital, Trondheim, Norway
2 SINTEF Community, Oslo, Norway
3 Department of Architecture, NTNU, Trondheim, Norway
* Corresponding author: mariuseb@stud.ntnu.no
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.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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