A significant barrier to the deployment of fault detection and diagnostics (FDD), artificial intelligence tools for system-scale optimization, and automation-assisted commissioning tools is the manually intensive process of mapping the building automation system data points to the applicable software tools. The necessary information about the mechanical systems design, point naming conventions used when installing the sensors and control systems, and other details, are contained in drawings, contract documents, and maintenance staff knowledge. Sometimes it is incomplete or partially wrong.
Achieving national goals for energy-efficient, grid-integrated buildings will require the use of well commissioned control systems combined with FDD and intelligent supervisory control processes that can respond to grid signals and user input. The focus of this project is to develop standards and other supporting infrastructure for representing the semantic information about the equipment, sensors, and actuators in a machine-readable way. It will include developing software tools to create building-specific models that conform to the standard using a combination of information available in BACnet systems and input from building operations staff.
Objective - To pursue an effort that results in national and international standards that define concepts and a methodology to create interoperable, machine-readable semantic models for representing building system information for analytics, automation, and control.
What is the technical idea?
The new technical idea is to adapt Semantic Web standards to the creation of formal models that represent building system components, their relationships in various contexts, and the associated data and control points. The Semantic Web is an extension of the World Wide Web that enables linkage of information, through knowledge graphs, located in different places and the encoding of metadata with that information that describes its semantic meaning. It is based on technologies such as Resource Description Framework (RDF), Web Ontology Language (OWL), and Semantic Web Rule Language (SWRL). These technologies can be applied in intelligent buildings domain to:
Building specific models created with this technology will enable building analytics and enterprise knowledge tools to automatically find necessary information to implement applications including:
What is the research plan?
ASHRAE has sponsored the development of proposed Standard 223, Semantic Data Model for Analytics and Automation Applications in Buildings. The development of this standard has been assigned to a working group of the ASHRAE BACnet committee that is being led by NIST staff. The working group consists of a group of over 30 technical experts from several countries. The development of the standard will leverage knowledge gained from earlier efforts to partially address this problem in the U.S. and in Europe including: Project Haystack, Brick, Building Topology Ontology (BOT), RealEstateCore (REC), Semantic Sensor Network Ontology (SSN) and Smart Appliances Reference Ontology (SAREF). It will also build on the QUDT ontology for measurements and units.
In addition to the standard development, NIST will partner with the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory, and the University of California at Berkeley to develop and test software tools that apply the draft standard to a number of specific applications that are envisioned for the standard. NIST will also work with BACnet International to develop conformance testing tools and processes that can be used to verify that a model created for a building complies with the requirements of the new standard.