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

Abstract

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.

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

“…Some types of schemas include relational database schemas and ontologies. An ontology is a formal model that allows knowledge to be represented for a specific domain; while a schema describes the types of things that exist, the relationships between them, and the logical ways those things and relationships can be used together [21]. Data platforms are used for storing and managing large amounts of data organized in a uniform manner using a specific data schema or ontology.…”

“…The 24 selected data tools were grouped into four categories, based on the use cases in which the data are involved, as well as their functionality during the data curation process: 1) Terminology: A collection of standardized definitions and terms. 2) Ontology: An ontology is a formal model that describes the types of things that exist, the relationships between them, and the logical ways those things and relationships can be used together [21]. 3) Schema: A data schema is the skeleton structure that represents the metadata and hierarchy view of the entire dataset.…”

“…These papers fail to review more common open data tools that may have less innovative features but are supported by government and industry (e.g., Green Button [20] used for smart meter data). Review papers on metadata schemas [12,21,22] show the variety of schemas used for different applications (e.g., Industry Foundation Classes [23] or Project Haystack [24]), but they do not provide detailed information on the actual implementation of these schemas into tools. A group of review articles summarized how the building data are supporting different analysis and applications: Molina-Solana et al reviewed how data science (algorithms and tools) has been applied to address the most difficult problems faced by practitioners in the field of energy management, especially in the building sector [25]; Volk et al focused on the development and evolution of the BIM application for existing buildings, and identified several BIM tools to employ [26]; Coakley et al conducted a review of using the measured data to support the development and calibration of the building energy model [27]; and Zhao et al presented different methods to predict the building energy consumption using measured and synthetic datasets [28].…”

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

“…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.…”

Development of a Unified Taxonomy for HVAC System Faults

“…Some industry efforts have produced industry available protocols to improve the semantic data interpretability in building applications such as Brick Schema [15], Project Haystack [16], and the ontology of smart building (SBOnto) [17]. A thorough review of these efforts can be found in [18]. Nevertheless, those protocols lack of capabilities to standardize FDD-reported fault data.…”