What is interoperability?
Smart buildings need communication, and open-source data tagging standards make that communication seamless. Securing the interoperability of components, no matter who developed the software or manufactured the devices.
Why smart buildings need data tagging standards
The increase in smart technology in built environments (aka Proptech) creates vast amounts of data. For this data to be useful, it must be accessible to all the linked systems and devices in a building. Standardizing the labeling of building components with a shared vocabulary makes this possible.
Benefits of standardized data tagging
Data tagging standards make data flow more quickly and efficiently, from their sources to controllers and between interrelated equipment. Standardized tagging and open-source data models enable better collaboration between subsystems and external data sources while facilitating data flow.
By standardizing semantic data models and web services, it becomes much easier to unlock value from the data generated by our smart devices.
Here are the top reasons why you should start using tagging standards in smart buildings:
Improved data quality
Data tagging standards ensure your data is labeled and categorized accurately and consistently. Reducing errors and inconsistencies that can pop up when data is labeled manually. Improving data quality will, for example:
- Help define the amount of data necessary to prepare new data sources.
- Allow you to find data more efficiently so it can be easily accessed when needed.
- Filter out unusable data or flag poor-quality data to secure better decision-making, including when utilizing machine learning algorithms.
- Improve the quality of big data gathered and allow building automation experts to use that unstructured and partially structured big data more readily.
Increased interoperability
Boost scalability by streamlining the integration of new software or devices. Data can be shared and analyzed across different systems and applications, and smart building systems can communicate more effectively with each other.
Better analytics
Help building managers understand classifications and relationships between building equipment. Accurate and consistent data labeling allows for more effective data analysis and helps us gain insights into energy usage, occupancy patterns, and other essential metrics.
Facilitates automation
By enabling smart building systems to recognize and respond to different types of data automatically, we are allowing for greater automation and efficiency in building operations.
Improved fault detection and maintenance
Significantly improve building fault detection and diagnostics capabilities. Labeling and categorizing data from various systems makes identifying and diagnosing system malfunctions or irregularities in their regular operation easier.
It also allows for implementing advanced analytics (Energinet) and machine learning algorithms (Edge AI) to detect patterns and anomalies in the data. The early identification of equipment faults, predictive maintenance, and proactive resolution of issues result in improved system reliability, reduced downtime, and optimized maintenance schedules.
Future-proofing
Sticking to data tagging standards ensures we build smart building systems with a shared framework. Integrating new devices and systems becomes easier and essentially future-proofing the building's technology infrastructure. Making sure it can adapt and evolve as new technologies emerge.
Potential drawbacks
Implementing data tagging standards in smart buildings can be complex and time-consuming, requiring significant resources and retrofitting efforts. Widespread industry adoption and support, ongoing maintenance and updates, training requirements, and scalability considerations are potential challenges to consider. Careful planning and evaluation of resources and industry support are necessary for successful implementation.
What open-source standards exist for building automation and IoT today?
BACnet is an open standard protocol for building automation and control networks that has been with us since the late 80s. Although primarily a communication protocol, it also includes a standardized data model. This data model can represent various building systems, such as HVAC, lighting, access control, etc. BACnet enables interoperability and integration between different building automation systems and devices. BACnet became ASHRAE/ANSI Standard 135 in 1995 and ISO 16484-5 in 2003.
Project Haystack seeks to standardize the way semantic tagging and data modeling are used in smart buildings for various applications, including HVAC, power management, lighting, shading, metering, fire detection, security, access control, CCTV, water leak detection, space management, asset management, etc. The project is managed using the Academic Free License 3.0.
The Brick Schema is a comprehensive set of tags and relationships that describe various building elements. These elements include rooms, HVAC systems, and sensors. Brick is free and open-sourced under the BSD 3-Clause license.
Other notable open-source initiatives are the Sedona Framework, a cross-platform software framework for device-to-device communication in building automation systems, and HyperCat, mainly focused on IoT in general. Still, it can also be applied to smart buildings.
How does Kiona use tagging standards?
Our building integration system, Web Port, uses customizable tagging standards to perform a wide range of automated connections.
With seamless integration and automation within smart buildings, system integrators can define and configure their tags and data models. Web Port's tagging system is flexible. It allows users to create a shared language for their building and equipment. This makes it simpler to connect and exchange data between different systems.
In addition to customizable tagging, Web Port offers a visual programming interface called Blockly. This interface allows users to create custom automation logic and workflows by visually connecting code blocks. Blockly enables users to use data tagging standards to define triggers, conditions, and actions. This allows them to customize automation scenarios according to their specific needs.
