A Match Made in Semantics: Physics-infused Digital Twins for Smart Building Automation

TL;DR

This paper introduces a physics-infused semantic approach using a bridging ontology to automatically match control programs to building systems, significantly reducing manual effort in building automation.

Contribution

It presents a novel high-level bridging ontology that enables fully automatic, rule-based matching of control programs to physical building systems using semantic relationships.

Findings

Over 90% accuracy in automatic matching of control programs.

Reduced manual effort by nearly an hour per match.

Validated approach in a real-life building automation project.

Abstract

Buildings contain electro-mechanical systems that ensure the occupants' comfort, health, and safety. The functioning of these systems is automated through control programs, which are often available as reusable artifacts in a software library. However, matching these reusable control programs to the installed technical systems requires manual effort and adds engineering cost. In this article, we show that such matching can be accomplished fully automatically through logical rules and based on the creation of semantic relationships between descriptions of \emph{physical processes} and descriptions of technical systems and control programs. For this purpose, we propose a high-level bridging ontology that enables the desired rule-based matching and equips digital twins of the technical systems with the required knowledge about the underlying physical processes in a self-contained manner. We evaluated our approach in a real-life building automation project with a total of 34 deployed air handling units. Our data show that rules based on our bridging ontology enabled the system to infer the suitable choice of control programs automatically in more than 90\% of the cases while avoiding almost an hour of manual work for each such match.