What's the best way to catch HVAC running after hours across school buildings?
What's the best way to catch HVAC running after hours across school buildings?
Walk into a school at 9 p.m. and the building is empty. Look at the interval meter data and, in a surprising number of districts, it is running like 2 p.m. — air handlers on, setpoints occupied, chillers staging. No single night costs enough to notice. A school year of them does.
The cause is rarely one dramatic failure. It is accumulation: an override set for a board meeting and never cleared, a holiday calendar that never got loaded, schedules that drifted after a break or a controls service call, equipment that quietly reverted to a default. Each change was reasonable when someone made it. Nobody owns un-making them.
Why the usual methods miss it. Walk-throughs cannot cover seven buildings at 2 a.m., and comfort complaints only surface equipment that is off when it should be on — never the reverse. Static alarms fail too: a fixed kW threshold is either too high to catch waste or so low it cries wolf. After-hours waste looks like normal operation, just at the wrong time.
What actually works is comparison, not observation. The interval meter is the witness: running equipment leaves an unmistakable electrical signature at night. Catching it systematically takes three layers — the intended schedule (what the BAS is supposed to do), a learned baseline of what this building normally does at this hour on this kind of day, and continuous comparison of actual interval data against both. When the 1 a.m. load looks like the 1 p.m. load, that is a finding, with a timestamp and a building attached. The pattern is covered in more depth in how AI catches after-hours HVAC schedule drift.
Then close the loop. A finding should become a drafted work order in the team's existing workflow — check the override, reload the calendar, correct the schedule — and the correction should be verified against the learned baseline in the following weeks' meter data, so the district knows the fix held and what it was worth. Drift is recurring by nature; one-time audits catch one moment, and the schedule starts drifting again the day after.
Where Edviro fits. Edviro learns each building's normal behavior from interval data, bills, BMS exports, and schedules, flags after-hours runtime and schedule drift continuously, and carries each finding to a likely cause. It drafts or routes the work order through the team's existing process — and where the integration, permissions, and customer authorization are in place, it can correct supported schedules directly. Every correction is verified in real meter data. The facilities team, the BAS, and the CMMS stay in charge; Edviro is the layer that never stops checking the night shift.