The Transition To Intelligent Monitoring Brings A New Era Of Efficiency And Cost Reduction For Building Operators.
In Part One of our series, “Why settle for “smart buildings”? “Intelligent” Building Monitoring is here today!” we discussed the current state of “Smart” building monitoring and defined the components of a robust “Intelligent” building monitoring platform. We will now focus on the adoption, setup, and value of implementing “Intelligent” building monitoring.
Where Are All The Intelligent Buildings?
The unfortunate truth is that the Buildings industry is far behind others in realizing AI’s promise. This is due to multiple factors, including the technology limits of legacy systems, the perceived upfront investment, a healthy, not-built-here mentality, and, in many cases, fear of change.
For years, the answer to the efficiency problem has had two solutions:
(1.) Continuously adjust and readjust the building systems when they drift out of their optimal parameters. The issue with this is that unless someone actively monitors the trends and tweaks all the building systems, the affected components could run out of sequence for months or even years before being reset. This process can leave a lot of cost and carbon savings on the table, not to mention the high operational overhead required to monitor and adjust systems over time manually.
(2.) Rip and replace old equipment with the latest and greatest. Depending on the age of the equipment being replaced, this either enables quick efficiency gains and operating cost savings or provides incremental improvements. In either case, the capital required to update the systems will take years to recoup. In some cases, this might be the answer, but the disruption to an occupied facility can be immense.
Now, There Is A Third Option: Intelligent Building Monitoring That Drives Results From Day One.
The good news is that configurable, off-the-shelf software now allows organizations to leverage their existing BMS and infrastructure to drive AI-based insights without the need for data science experts or expensive and time-consuming deployments.
By connecting existing building management workflows to automated solutions that utilize AI-based auto-tagging, equipment-specific AI deployment templates, and a simplified interface, users can realize substantial time and resource savings even before intelligent monitoring begins. The automated software delivers the system upgrade without disrupting existing workflows or large equipment expenditures.
This automated approach removes heavy lifting from the customer or integrator and places it on the software, from data tagging, configuration, and model selection to training, deployment, and model management. By delegating these tedious and time-consuming functions to the software, stakeholders can realize a quick ROI while limiting downtime and disruption. These end-to-end solutions are available today and can be deployed in as little as an afternoon.
How Does Intelligent Monitoring Create Value For Building Owners, Operators, and Tenants?
To answer this question, we will examine each of the four components of an intelligent monitoring program in greater detail. As we know, every building is a snowflake, so the results will vary from study to study and use case to use case. The data below was provided by reputable sources, but we always tell customers to consider the data as directional indicators vs. hard and fast facts. However, based on the ever-growing mountain of evidence, it is undeniable that AI-based intelligent monitoring delivers tangible top and bottom-line results. From increased tenant satisfaction to reduced energy and maintenance costs, the business case is clear.
Fault Detection and Diagnostics (FDD)
Fault detection and diagnostics powered by Artificial intelligence (AI) represent a significant advancement with profound implications for energy efficiency, sustainability, and cost reduction in the buildings space.
Energy Efficiency: Automated AI solutions play a pivotal role in optimizing energy usage within buildings by identifying and rectifying inefficiencies. Sophisticated algorithms analyze vast amounts of data to pinpoint energy waste and recommend targeted solutions. A recent study by the Department of Energy and Berkeley Labs of 6500 buildings found that the median organization using a Fault Detection and Diagnostics (FDD) platform saved $3 million annually or 9% in energy savings. This reduction in energy usage translates directly into substantial savings on utility bills and contributes to the overarching objective of reducing carbon emissions.
Operational Optimization: By continuously monitoring building systems and equipment, AI-enabled fault detection and diagnostics drive optimal performance levels while minimizing waste. According to the IEA, AI-based FDD’s ability to detect and address faults promptly not only enhances operational efficiency but also extends the lifespan of equipment by as much as 20%, reducing the need for premature replacements and associated expenditures.
Cost-Benefit Analysis: The financial implications of energy-saving initiatives derived from AI are substantial. Research from the US Department of Energy underscores the potential cost savings associated with leveraging AI for fault detection, ranging from 5% to 30% in annual energy costs. Moreover, proactive identification and resolution of equipment malfunctions can lead to significant reductions in maintenance expenditures, further bolstering the economic case for AI-driven FDD.
Long-term Sustainability: From a long-term perspective, integrating AI into building management systems aligns with sustainability objectives by promoting energy conservation and reducing environmental impact. By optimizing energy usage and minimizing carbon emissions, AI-driven FDD contributes to the broader goal of creating sustainable built environments.
Functional Digital Twins
AI-driven digital twins enable building operators to make data-driven decisions, optimize resource allocation, and prioritize maintenance tasks effectively. This ensures the sustained performance and longevity of building infrastructure while driving tangible financial and environmental benefits.
Scenario Planning: AI-driven digital twins enable scenario planning, aiding in predictive modeling and optimizing resource allocation for maintenance and upgrades, resulting in better decision-making. Research from Mordor Intelligence indicates that organizations using digital twin technology report a 30% increase in scenario planning accuracy over traditional methods.
Cost Reduction: AI-driven digital twins contribute to significant cost reductions. A recent study from Deloitte shows that incorporating digital twins can lead to a 40% decrease in maintenance costs and a 25% reduction in energy expenses.
Tenant Satisfaction: According to Harvard Business Review, buildings equipped with AI-driven digital twins experience up to a 15% increase in tenant satisfaction levels, as these systems ensure optimal comfort and operational efficiency. Enhanced tenant satisfaction also translates to improved retention rates and a positive brand reputation.
Equipment Health: AI-driven digital twins monitor equipment health in real time, detecting anomalies and predicting potential failures before they occur. This proactive approach extends equipment lifespan, reduces downtime, and increases operational reliability. Another recent study from McKinsey & Company found that organizations utilizing digital twin technology reported a 20% decrease in equipment downtime.
Energy Forecasting And Optimization
AI-based energy forecasting and optimization offers multifaceted benefits to building management, ranging from sustainability and cost savings to tenant satisfaction and improved building health. With the growing emphasis on energy efficiency and environmental stewardship, integrating AI technologies into building operations is becoming increasingly essential for achieving long-term success and resilience.
Enhanced Sustainability: McKinsey & Company reports a potential 20% reduction in energy consumption through AI-powered optimization in buildings, which would contribute significantly to lower carbon emissions and environmental sustainability.
Improved Tenant Satisfaction: Research by the Urban Land Institute indicates that buildings equipped with AI-driven energy optimization systems witness higher tenant satisfaction rates, fostering optimal indoor comfort levels while maintaining energy efficiency.
Cost Savings and ROI: The International Energy Agency estimates potential cost savings of up to 25% for building owners and operators through AI-enabled energy management solutions.
Advanced Analytics
Advanced analytics in building management is not a new concept, but it still plays a pivotal role in advancing energy efficiency, sustainability, and operational optimization. The combination of rules-based analytics and AI provides a complete picture of building systems, enabling users to pinpoint and address inefficiencies while further reducing unplanned downtime and costs.
Energy Efficiency: By harnessing advanced analytics, building managers can optimize energy usage by identifying and rectifying inefficiencies as they occur. Research from McKinsey & Company suggests that buildings equipped with advanced analytics systems can achieve energy savings of up to 20%.
Operational Optimization: Advanced analytics delivers real-time insights that building operators use to drive optimal performance levels while minimizing waste. These analytics-driven optimizations not only improve building performance but also contribute to cost savings and resource conservation.
Cost-Benefit Analysis: The financial implications of advanced analytics in building management are significant, the realtime identification of equipment malfunctions and inefficiencies leads to significant reductions in maintenance expenditures.
Long-term Sustainability: By optimizing energy usage and minimizing carbon emissions, advanced analytics contributes to the creation of sustainable built environments, fostering long-term environmental stewardship and resilience.
In Summary
Intelligent building monitoring drives efficiency and sustainability while increasing tenant satisfaction, uptime, and financial returns. Regardless of operational scale, these automated solutions remove many of the hurdles associated with modernizing our industry. The best part is that AI software requires your building’s data as fuel, not your checkbook.
Contact Elipsa, And Let Us Show You What Others Only Talk About.
Elipsa provides automated AI-based predictive solutions that monitor and optimize critical building equipment and systems. It 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.
Contact Elipsa for your free consultation, or to learn more, visit www.elipsa.ai