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More building data isn’t necessarily better.
More data, without proper analysis, results in an ever-expanding haystack, making it harder to find the needle — the actionable takeaway.
Today’s connected buildings can generate petabytes of data each day — far more than human intelligence can analyze on its own. I’ve tracked the facility management industry for over 10 years, and I can tell you, most building data infrastructure isn’t able to keep up with the age of IoT. The result is that most building data remains in the dark, sitting unstructured and unanalyzed in lonely servers.
Older systems may work well enough for some building owners and managers, but what savings are you leaving on the table, and what faults are you failing to prevent?
The answer, it turns out, can be quite a lot. In this blog, I’ll look at how one university saved nearly a million dollars in energy costs in a single year. It did this by adding an AI layer onto its existing building management system (BMS), enabling automated fault detection and diagnostics with predictive maintenance.
How one university avoided $900,000 in yearly energy waste
The University of Iowa is a thriving campus home to over 30,000 students and dozens of buildings. With the goal of deepening its commitment to sustainability, the university sought to get more out of its building data. The campus already had a BMS, but it was not connected to any AI tools or robust data management system.
The university, in collaboration with Schneider Electric™ and one of our EcoXpert™ partners, Control Installations of Iowa, Inc., developed new data infrastructure. This infrastructure was built with EcoStruxure Building Advisor, a solution combining IoT-connected devices, AI building analytics, monitoring software, and expert services. The goal was to go beyond reactive maintenance and achieve predictive maintenance.
Using artificial (and human) intelligence to analyze building data
To achieve predictive maintenance, machine and human intelligence had to work together. Two heads are better than one, but two heads and dozens of petabytes of analyzed building data are even better. By connecting existing building devices to cloud analytics, there was more data, but there was far more brainpower to analyze that data.
The result: Instead of identifying issues based on occupant complaints, the university used cloud analytics to detect faults automatically before they caused issues such as overly warm/cool rooms or waste. These building analytics ran through stacks of data, identifying trends and anomalies that would’ve otherwise remained invisible.
The university didn’t stop there. To make sure these actionable insights wouldn’t go ignored, it formed an analytics response group that routinely discussed the AI’s recommendations. The team prioritized tasks that would most increase occupant comfort and operational efficiency. These tasks ranged from preventive maintenance actions to adjusting system usage based on occupancy trends. Part of the Building Advisor solution is to couple AI analytics with remote service experts, who can help facility managers make sense of the findings. Armed with this data, the analytics team set out to optimize the campus’ energy efficiency.
Small fixes, big savings
The new building analytics solution quickly delivered results. It detected a previously invisible fault: Over the winter, a mechanical fault in a large piece of equipment in the central plant caused additional cooling, resulting in reheating on the airside — a classic case of an HVAC system fighting itself.
Despite the system working overtime, the room temperature remained within the normal range, so neither the BMS nor the building occupants noticed. Once Building Advisor was activated, it noticed an overworked chiller and notified the team of the fault.
With a simple mechanical fix of reconnecting the valve to the control, the university prevented thousands of dollars in monthly energy costs. Through fixes like these, the university saved $900,000 on energy costs in the first year alone.
What energy savings could you find?
Stories like University of Iowa’s are increasingly common, and there are energy savings waiting to be discovered. The International Energy Agency found that digitizing buildings through IoT and cloud analytics could shrink total building energy use around the world by 10 percent. But so far, using AI-driven analytics on building data is still in its infancy.
Ready to change that? Find out how you can jump ahead of the AI curve on our Building Advisor page.
