Formalizing Tag-Based Metadata With the Brick Ontology

Fierro, Gabe; Koh, Jason; Nagare, Shreyas; Zang, Xiaolin; Agarwal, Yuvraj; Gupta, Rajesh K.; Culler, David

doi:10.3389/fbuil.2020.558034

Cited by 11 publications

(9 citation statements)

References 25 publications

(22 reference statements)

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“…Fourteen different schemas have been identified that describe metadata about controls and metering in commercial buildings, but from this high-level review, the overlaps between them and their gaps are unclear. An extensive comparison between Brick and Project Haystack is presented in [148], but no publication compares all these schemas in depth. Gilani et al tried to evaluate the scope of several ontologies by identifying what type of data they describe (e.g., external conditions, energy use), but these categories are too broad to clearly identify the differences between schemas.…”

Section: The Landscape Of Metadata Schemas For Building Energy Applicmentioning

confidence: 99%

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

et al. 2021

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Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases.

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Section: The Landscape Of Metadata Schemas For Building Energy Applicmentioning

confidence: 99%

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

et al. 2021

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“…Brick, conversely, is a more structured, formal, and expressive ontology, but it requires more specialized software tools, and it is still under development. A more formal comparison of the two schemas is presented by Fierro et al [75].…”

Section: Table 2 Summary Of the Selected Data Tools: Unique Features mentioning

confidence: 99%

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

Luo

Pritoni

Hong

2021

Renewable and Sustainable Energy Reviews

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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

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“…In addition to development in occupant-centric metrics and occupancy sensing technologies, there has been significant advancement in the standardized representation of operational building data [27][28][29]. Many different forms of digital information are produced over the lifespan of a building, capturing design, construction, commissioning, operations and controls, maintenance, and audit information.…”

Section: Developments In Data Schemas For Buildingsmentioning

confidence: 99%

“…Until recently, neither the schema nor accompanying documentation were provided in a machine readable format. The newest version, Haystack 4 (currently in prerelease), has addressed this while also defining concepts in a taxonomic structure to address some previous critiques [28]. The Brick Schema was developed to provide a more formalized class hierarchy than Haystack 3 originally implemented.…”

Section: Developments In Data Schemas For Buildingsmentioning

confidence: 99%

“…While it is well known that Haystack and Brick are used by energy management information systems (EMIS) to support energy analysis and fault detection and diagnostics applications (FDD) [28,37], documentation for the implementation of algorithms using standardized concepts defined by either schema has not been demonstrated. Although detailed descriptions of control and FDD algorithms are provided in many previous publications [38][39][40][41][42], they merely include point name descriptions and don't reference any data schema objects, which we believe is a serious opportunity missed and a novelty of the research performed by our study.…”

Section: Point Of Departurementioning

confidence: 99%

See 1 more Smart Citation

Development and Application of Schema Based Occupant-Centric Building Performance Metrics

2021

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Occupant behavior can significantly influence the operation and performance of buildings. Many occupant-centric key performance indicators (KPIs) rely on having accurate counts of the number of occupants in a building, which is very different to how occupancy information is currently collected in the majority of buildings today. To address this gap, the authors develop a standardized methodology for the calculation of percent space utilization for buildings, which is formulated with respect to two prevalent operational data schemas: the Brick Schema and Project Haystack. The methodology is scalable across different levels of spatial granularity and irrespective of sensor placement. Moreover, the methods are intended to make use of typical occupancy sensors that capture presence level occupancy and not counts of people. Since occupant-hours is a preferable metric to use in KPI calculations, a method to convert between percent space utilization and occupant-hours using the design occupancy for a space is also developed. The methodology is demonstrated on a small commercial office space in Boulder, Colorado using data collected between June 2018 and February 2019. A multiple linear regression is performed that shows strong evidence for a relationship between building energy consumption and percent space utilization.

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Formalizing Tag-Based Metadata With the Brick Ontology

Cited by 11 publications

References 25 publications

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

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

Development and Application of Schema Based Occupant-Centric Building Performance Metrics

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