How Human Behavior Data Is Reshaping Campus Facilities Management
The ebb and flow of students, faculty, and administrators across a campus have a larger impact on maintenance, cleaning, and sustainability than many realize. From requiring students and professors to take shuttle buses to sit in underoccupied lecture halls, to impromptu study groups in the student union, to unexpected spikes in energy consumption, these everyday activities create unnecessary strain on facilities teams and drain the infrastructure.
3 Factors Disrupting Facilities and Maintenance Teams
Consider the typical mid-size campus. Administrative offices are often filled from 9-5 on Monday through Friday. Meanwhile, students are known to keep erratic hours. Dorms and libraries can be buzzing or relatively quiet depending on weather, the percentage of quiet floors and study lounges, and varied schedules. These spikes in foot traffic and occupancy often lead to an unbalanced workload for maintenance crews while increasing a building's carbon footprint.
This leads to the second factor disrupting facilities. When lighting and HVAC systems run on fixed schedules, they continue to heat and cool spaces based on time of day, not occupancy or demand. Even when you have a sense of which buildings will be busy at specific times, it can still be hard to anticipate occupancy demands. The result is higher cleaning and maintenance costs because they are based on schedules, not usage. It is estimated that having usage data can save an average of $1,400 a year on cleaning, maintenance and replenishment costs for a single restroom.
Additionally, a common facilities issue that often happens in the winter is the heat being set to steadily increase in the morning as the campus comes alive. By late morning, the heat is at its highest level. In turn, this sends the system into a cool-down mode. In this heating/cooling cycle, people on campus are constantly dealing with uncomfortable indoor temperatures.
The third factor impacting the built environment on campuses is the rise of blackouts. On campuses, AI is driving demand for higher performance computing and data centers to power research and labs. Adding to this is the high-speed computing needs of students, faculty, and staff that continues to strain the power grid. The problem accelerates when you factor in the aging infrastructures of older universities. Several industry benchmarks cite an estimated $100,000 per hour is lost during a power-related outage. Adding to the urgency to address the issue is a July 2025 warning from the U.S. Department of Energy that blackouts could increase by 100 times in 2030.
In response, universities are building their own private, renewable power sources. The goal, of course, is to reduce reliance on an external grid to ensure campus life is not interrupted. The tradeoff is the required investments in infrastructure, especially retrofitting older buildings, to make this vision a reality.
Some campuses that have successfully tackled this issue include UC San Diego, whose microgrid powers 90% of campus needs, and Stanford University, which applies "islanding" so it can continue to operate independently from the grid during an outage. These university microgrids are not backup generators. Instead, they are sophisticated systems that demand precise data on building usage to effectively handle spikes.
These three converging factors — erratic building occupancy, fixed HVAC systems, and increasing demand for power — explain why campus facilities teams are seeking granular insight into their buildings. Accurate insight into how the campus is used based on human behavior, not assumptions tied to schedules, can result in more efficient buildings.
The Controversy Over Capturing Building Data
The ability to understand human interactions indoors and gather operational building data has long been a priority for stakeholders responsible for the built environment. Yet the ability to capture that data has often required cameras. Along with putting FERPA compliance at risk, cameras are off-putting to students and faculty alike. Today, thermal sensors, which are incapable of identifying individuals, are being installed at large campuses to better understand how humans interact indoors without compromising privacy.
For example, a large university in upstate New York installed wireless sensors in several buildings to capture anonymous data on occupancy, foot traffic, impromptu huddles, and spikes in energy demand. The facilities team input that data into their existing building management platform and discovered that the layout of some common areas, as well as maintenance schedules, did not align with the needs of the people on campus.
This led to changes in the layout, cleaning schedule, and timing of heating/cooling systems. Essentially, they scheduled cleaning and maintenance based on behavior, not clocks. The result is more efficient buildings and fewer complaints.
As an additional benefit, having granular data on human behavior can lead to assigning lecture halls based on proximity of the professor's office and student schedules, therefore putting less strain on shuttle buses.
The future of the campus, as both a workplace and living environment, hinges on the built environment's ability to be more responsive to humans. By understanding human behavior based on aggregate data, not invasive monitoring, facilities and maintenance teams can move away from structured schedules to more dynamic operations models.
About the Author
Honghao Deng is CEO and co-founder of Butlr.