A Reference Architecture for Data-Driven Smart Buildings Using Brick and LBD Ontologies

Pieter Pauwels, Gabe Fierro

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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With the increasing adoption of sensors, actors and IoT devices in existing buildings, the real estate sector is becoming increasingly automated. Not only do these devices allow to monitor these buildings (energy use, occupancy, indoor air quality, etc), they also enable model predictive control (MPC) through building automation and control systems (BACS). A critical feature to enable these is the metadata associated to data streams obtained from the building. Such metadata allows building operators to assess what these data streams are, what they are measuring and how. This can be achieved using metadata schemes and vocabularies, such as Brick, Haystack, Linked Building Data, Industry Foundation Classes. Merging these model-based metadata schemas (semantics) with data-driven monitoring and control (machine learning) into a functional system architecture is a considerable challenge. In this paper, we review the mentioned technologies and propose a draft reference architecture based on state-of-the-art research. This reference architecture is evaluated using a set of predefined criteria.
Original languageEnglish
Title of host publicationProceedings of the REHVA 14th HVAC World Congress (CLIMA 2022)
Publication statusAccepted/In press - 2022
Event14th REHVA HVAC World Congress, CLIMA 2022 - Rotterdam, Netherlands
Duration: 22 May 202225 May 2022
Conference number: 14


Conference14th REHVA HVAC World Congress, CLIMA 2022
Abbreviated titleCLIMA 2022
OtherTowards digitalized, healthy, circular, and energy efficient HVAC
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