Converting abnormal HVAC operation into measured savings
At a second commercial banking space, Renergia's AI analysis detected abnormal HVAC operation outside the required schedule. The client accepted the automation recommendation — and the measured, weather-adjusted result was a 60.5% reduction in out-of-hours consumption.
Context
The case covers a real, anonymized commercial banking space with no on-site photovoltaic production, so every kilowatt-hour saved directly reduces imported energy. The analysis focused on HVAC operation and out-of-schedule consumption in the cold-season profile.
Renergia used total load, HVAC load, and hourly outdoor temperature from February 2026 monitored data. The automation was implemented on 18 February, and direct savings were measured over the confirmed low-occupancy window from 19 to 28 February.
18 Feb 2026
Automation implemented
19–28 Feb
Measured savings window
No PV
Savings reduce imported energy directly
What Renergia found
Renergia's AI-based automated analysis compared the real hourly consumption profile with the expected technical reference profile, adjusted for outdoor temperature — and detected heating operation continuing well outside the required schedule.
The disaggregation algorithm isolated the HVAC component from the total load, making visible the consumption most likely driven by heating operation outside the required schedule.
How the issue was identified
The analysis separates the effect of weather from the effect of HVAC automation: colder hours are accounted for before attributing any savings to the measure.
What we did
Renergia proposed HVAC automation through the platform. The client accepted the recommendation and the measure was implemented from 18 February 2026, targeting the 19:00–07:00 low-occupancy window.
The direct savings calculation deliberately excludes two operational windows:
Pre-heating
The morning interval before the space opens
After office
The transition period after normal hours
These periods may still be operationally justified, so they were not counted as direct savings.
Expected vs. real
In the observed 19:00–07:00 window, the temperature-adjusted comparison shows the measured effect of the automation.
Technical reference consumption expected in the observed window under the recorded outdoor temperatures.
Measured consumption after implementation — 2,526 kWh saved in ten days.
Measured results
The observed window delivers a measured result; the seasonal figures are an orientative extrapolation based only on the February profile, without invented temperatures for other months.
Total load, 19:00–07:00, adjusted for weather. Avoided emissions: 0.44 t CO₂.
Technical confirmation of the measure — an 88.6% HVAC reduction in the main window. 0.14 t CO₂.
Orientative extrapolation for the five-month period. 6.60 t CO₂.
Monthly scenario derived from the February profile.
Financial values use an exchange rate of 5 RON = 1 EUR.
Why it matters
Renergia converted raw energy data into a measurable operational result. The AI-based analysis detected abnormal HVAC consumption, the disaggregation algorithm isolated the HVAC component, and the implemented automation reduced unnecessary out-of-schedule operation.
Because the savings are measured against a temperature-adjusted reference, the result reflects the automation itself — not a milder week of weather.