An overview of data tools for representing and managing building information and performance data

Abstract

Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings-a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable. Highlights:• Building information and performance data are grouped into 10 typologies • 24 data tools are selected, categorized into 3 groups (terminology, ontology/schema, platform), and reviewed • Most of these tools can be further enhanced to improve flexibility, standardization, and interoperability 2 • Ontologies or schema to represent metadata of occupants and basis of building design need to be developed • Integrating building data across the building life cycle to support various use cases remains a challenge

Building Information Modeling (BIM) Capabilities in the Operation and Maintenance Phase of Green Buildings: A Systematic Review

“…In the operation and maintenance phase of green buildings, with the integration of the BIM and building management system, the produced information (including daily facility management activities, energy consumption information, maintenance and repair records, renovation and refurbishment activities, safety management information, security records, space and asset utilization situation, etc.) can be retrieved, stored, and categorized automatically [47][48][49][50]73,98,102,122]. Besides these, with the integration of BIM and GIS (Geographic Information System), environmental information (including topography, terrain, hydrology, vegetation, road, utility, infrastructure, etc.)…”

Extending the Brick schema to represent metadata of occupants

“…Luo's review [22] covers existing data schemas for occupants. Two essential ones are discussed here.…”

“…Adopted as an international standard, IFC was developed to represent the BIM data across the whole building life cycle, from building design, building construction, to building operation and other implementations. Based on a recent review paper, the IFC schema is also capable of representing the occupant-related data [22]. We assessed the data coverage of the IFC schema, and compared that coverage with those using the original and extended Brick schema.…”

“…Metadata are descriptions of data which provide contextual information and thus assist in the discovery of that data on behalf of applications and other data consumers. A recent review of existing data schemas and tools indicates that some data schemas, such as Brick and Green Button, are capable of representing the metadata of indoor environmental data, building and system operational data, and meter data [22]. However, ontologies or schemas representing occupant data (especially their metadata) are very limited, which hinders the use of the data to gain a deeper understanding of HBI and associated insights to improve building design and operations.…”

Mapping of extensible markup language-to-ontology representation for effective data integration

“…However, XML data represent the data only at the syntactic level. On the other hand, ontology is a knowledge representation that established a shared vocabulary, conceptualizations and model domain knowledge for various applications [5]- [8]. Ontology is often expressed in ontology web language (OWL) format.…”