Tag Your Building In Record Time: Simple And Scalable AI
Tagging building HVAC data, with a tagging ontology such as haystack, is critical for enhancing the functionality, efficiency, and effectiveness of HVAC systems within a building. By improving data organization, facilitating predictive maintenance, enabling better integration with building management systems, and supporting advanced analytics, tagging contributes to significant operational improvements, energy savings, and enhanced occupant comfort.
Let’s paint the picture of a perfect building:
1) You have vast amounts of data that is well organized making it a cinch to retrieve and manage specific information. You instantly know the type of every piece of equipment and each of their data points such as temperatures and pressures
2) You have detailed real-time monitoring and diagnostics on your systems. Quickly identify issues such as abnormal temperatures or faulty components
3) Your equipment is operating efficiently ensuring low costs and energy consumption. HVAC operation is fully optimized identifying patterns of inefficiencies and automatically adjusting controls to reduce operational costs
4) Advanced analytics such as machine learning is proactively monitoring for issues before they occur. Predict component lifespan and prevent unexpected downtime.
5) Enhance your equipment’s integration with your building management system. Seamlessly connect all equipment and data to your BMS to enhance your overall control and automation. All of your data is standardized and normalized across your equipment and buildings
6) Full reporting and compliance. Your detailed reports ensure compliance with regulatory requirements and industry standards. Seamlessly retrieve data for audits and regulatory inquiries.
7) You provide the highest level of user experience and comfort. Automatically adjust settings based on current conditions and occupancy to provide optimal comfort levels across various areas of your building
8) Your building is generating advanced insights. Apply advanced analytics to gain a deeper understanding of your equipment. Identify trends and anomalies showing areas of improvement and insights for building managers
Tagging Unlocks Your Building’s Potential. So Why Don’t People Do It?
All of this is possible today and easy to implement if you do one thing, tag your data.
With a data model such as project haystack, you can add standardized meta data to your equipment and points to unlock the tremendous amount of value stored within your building.
All of this value that we just laid out can be yours. But more often than not the response we hear is “nobody tags”.
On one hand, this utopian building is at your fingertips thanks to tagging. So what is the road block? What is the reason behind the answer “nobody tags”? The answer is time.
The time it takes you or your system integrator to add these tags to your data is far too cost prohibitive that it’s nearly impossible to expect a return on that investment. As a building owner or a systems integrator, if you had a proposal in front of you that included tagging and one that would not, it’s nearly impossible to get over the sticker shock of the time and materials needed to tag for you to fully see the value at the end of it.
So, nobody tags.
But what if time was not an issue? What if you could do it for half the cost, a tenth of the cost or even less? What if you could do this because it was not your SI pouring over a screen for hours but instead it was AI analyzing your building in seconds?
Auto-Tag Your Building Data with AI
Artificial Intelligence is meant to mimic human intelligence. One such area is natural language processing and the ability to understand language and context.
A human could look at an equipment name in your BMS such as AHU_Lobby or Chlr1 and reasonably determine that you are looking at an AHU and a Chiller. Similarly, you can safely assume that DATemp in a VAV is discharge air temperature or FanStatus is, well Fan Status. AI can be utilized to do the same.
One big problem across the industry is that point names are hardly ever consistent. That is true across the same building, never mind across different buildings programmed by
different engineers. So, that is where tagging comes in to have a standard set of normalized meta data to help applications know what equipment and points are.
AI can cut through the grunt work. Using knowledge gained from previous buildings, AI can predict the equipment and point types similar to how a human would. Once the type is known we can automatically assign standard tags to the equipment and points. With the assistance of AI, the process of tagging thousands of points, which used to take hours, can now take minutes.
The time hurdle has been eliminated and the fully optimized and monitored building that we described earlier is now a possibility.
To learn more, and to see how auto tagging works, contact us at info@elipsa.ai
