AI-driven fault detection, energy optimization, and digital twins are transforming building operations through end-to-end intelligent monitoring.
In the world of building management, reliability, availability, and life span of your assets are essential in delivering productive, safe, and sustainable environments. Automated AI solutions unlock substantial energy savings, increase operational resilience, and deliver on sustainability objectives for building operators at any scale.
Over the past decade or more, equipment manufacturers in the building space have incorporated IoT connectivity into their solutions to help drive efficiencies and increase visibility. This new connectivity has been an essential step in the evolution of building tech for owners, building management teams, and tenants. Anyone with access to a BMS (building management system) can now see under the hood of those expensive metal boxes that reside on the roof or in the basement. A modern BMS will provide insight into what the system is doing at a given time. Armed with this newfound insight, we have deemed our buildings "Smart."
What Is "Smart Monitoring"?
Smart is defined as embedding an inanimate object with network connectivity and enabling it to communicate the object's status, performance, or behavior.
When applied in the buildings space, this may mean reporting the supply temperature in the various HVAC systems, the status of a boiler, the temperature in the lobby, the amount of electricity consumed, or even the water pressure in the fire suppression system.
All this information is critical to making informed decisions based on the current state of the systems. Smart is akin to looking at the instrument panel in an airplane cockpit. You can see the wind speed, airspeed, altitude, pitch, etc. The challenge is that there is no windshield. A BMS provides historical and current system information but lacks the ability to see what is coming.
Here is where AI-based intelligence comes in. Smart systems provide critical information for the here and now, whereas intelligent systems also allow you to anticipate and prepare for the future. By incorporating intelligent monitoring, users can see out the windshield and get the complete picture of their building systems' past, present, and future state.
What Is "Intelligent Building Monitoring"?
Intelligent building monitoring takes into account all the relevant equipment data (not just what you might track on a dashboard) and then applies artificial intelligence and machine learning to uncover data-driven predictions. These AI-based predictions provide insight and guidance far beyond traditional systems and methods.
AI constantly learns how a specific facility (and the components that make up the building systems) reacts under different circumstances. Because the data used to train the AI is derived from equipment in its real-world environment, the AI model is able to parse, prioritize, and decipher accurate predictions. Continuous learning allows the AI to evolve with the equipment over time while improving prediction quality and operational effectiveness.
Accurately predicting future events gets the lion's share of attention, causing many to overlook the fact that AI also enhances real-time monitoring. By establishing baselines and identifying correlations, AI-based systems are able to identify outliers that may be missed by traditional rules-based methods in real-time.
By understanding the future status, performance, and behavior of the HVAC equipment and other critical building systems, operators can manage and deploy resources more efficiently while ensuring safe and productive built environments.
What Are The Components That Make Up A Robust Intelligent Monitoring Program?
Depending on the vendor you talk to, this can range from rules-based alerts masquerading as AI-based predictions to fully automated building management where the AI controls all the set points and other features (typically large, expensive, custom projects still in beta) and everything in between.
Too many vendors focus on adopting the latest (loosely defined) buzz terms instead of clearly stating their solution capabilities. Due to this lack of transparency and a clear delineation between legacy technologies and what is now possible through AI, the rabbit holes seem endless.
In recent years, many legacy vendors have developed a witch's brew of rules-based solutions, mixing in the term AI without explaining where or how it is used. This ambiguity leaves stakeholders in a constant state of analysis paralysis. Waiting to hear the buzz terms in the right order.
From our perspective (and we feel confident in our assessment given the hundreds of end-user, partner, and vendor discussions we have had), a modern Intelligent Monitoring program has four key components:
Fault Detection and Diagnostics (FDD)
FDD systems utilize data from critical equipment to analyze performance patterns and detect abnormalities that may indicate faults or inefficiencies. By pinpointing these issues early using AI, FDD helps optimize system operation, improve energy efficiency, and reduce maintenance costs while enhancing occupant comfort and safety.
Functional Digital Twins (FDT)
FDTs are virtual models that replicate the behavior and performance of physical systems or equipment in real time and, when coupled with AI, will predict the future state of the equipment in a given scenario. These digital replicas utilize real-world data from sensors, controls, and other sources to simulate operation. By providing insights into system behavior, performance, and potential faults, Functional Digital Twins enable predictive maintenance, optimization of energy usage, and overall improved management of building infrastructure without disruption to the actual physical equipment.
Energy Forecasting and Optimization (EFO)
Energy forecasting and optimization involve predicting energy consumption patterns and optimizing system operation to minimize energy usage while maintaining desired comfort levels. Energy forecasting entails using historical data, weather forecasts, occupancy patterns, and other relevant factors to predict future energy demand. This forecasting helps building managers anticipate when energy usage will peak and plan accordingly to reduce costs and increase efficiency.
Advanced Analytics:
Advanced analytics refers to the use of sophisticated data analysis techniques to extract insights, optimize performance, and improve efficiency in the operation and maintenance of building systems. This involves leveraging data collected from various sensors, equipment, and building management systems to gain a deeper understanding and make informed decisions.
As we discussed, the delineation between legacy and AI-based systems is hard to decipher given the buzz terms and hype that marketers inject into the storyline, but we hope this provides some clarity and guidance around what to look for in a modern intelligent monitoring solution.
In the second installment of this series, "From Smart to Brilliant: The Power of Intelligent Building Monitoring,"Â we will discuss the value proposition and adoption of intelligent building monitoring.
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Elipsa provides automated AI-based predictive solutions that monitor and optimize critical equipment and systems within buildings. Elipsa is device and platform-agnostic, enabling organizations to connect their existing systems via MQTT, Spark Plug B, and the Niagara Data Service. Elipsa's automated platform handles all the data science and deployment, enabling simple, fast, and flexible building optimization.
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