What was measured
Over a complete annual cycle, two residential care facilities cut the energy they consumed by 35 per cent. It is a weighted average across the whole year, normalised for weather and price, and it corresponds to an avoided cost of 239,000 euros across the two facilities over the twelve months.
| Result | Value | Perimeter |
|---|---|---|
| Normalised annual saving | −35% | average of the two facilities, 12 months |
| Avoided cost | 239,000 euros | actuals, two facilities |
| Peak in the winter period | −58% | January-February, measured |
The method is IPMVP Option C, meaning whole-facility measurement from the main meter, with climate normalisation on heating degree days at an 18 °C base and price normalisation. It is the option you use when several systems are addressed at once and you do not want to credit a single intervention with everyone else’s results.
The two facilities are referred to as Facility A and Facility B. The results are real, the clients are not identified.
How the saving moved through the year
The saving was not uniform, and the way it varies says more than the average does.
| Period | Saving |
|---|---|
| January-February | −57% |
| March-April | −42% |
| May-June | −24% |
| July-August | −18% |
| September-October | −30% |
| November-December | −49% |
The two-month figures are rounded: the certified data are the annual average, the avoided cost and the winter peak. In January-February, taken facility by facility, the results were 58 and 56 per cent.
Why the annual figure is lower than the peak
Anyone reading a table like this asks the same thing: if January hit 58 per cent, why is the average 35?
The saving is at its highest when the heating is working. In the cold months, optimising the thermal plant has a great deal to work on, and it does. That is where both the consumption and the room for manoeuvre are concentrated.
In the shoulder and summer months the thermal loads fall. With less load to optimise, both the absolute and the percentage saving shrink. It is not the system performing worse: there is less to recover.
The 35 per cent is the weighted average across the whole year, normalised for weather and price. It is the number to use in a business case, because it is structural and repeatable. The peak of a single two-month window is not, and presenting it as an annual result is the fastest way to lose credibility at the first check.
There is a second reason, and it is only fair to state it: the rising trend of the saving through the year also reflects the gradual activation of the controls on the various systems, not a change in the system’s efficiency.
Six control loops, no building work
The result does not come from a single intervention but from several automatic controls, switched on progressively. All of them are driven by the sensors, none requires manual action by staff, and none meant shutting down plant to install it.
1. Occupancy through CO₂, the main intervention. Wireless CO₂ sensors in every room: in an occupied room the concentration rises, in an empty one it falls. After one or two hours of a room being free, the system recognises the absence and modulates switching and setpoints. When the room is reoccupied, comfort is restored ahead of time, not when someone walks in and feels cold.
2. Window sensors. Not wired. With conditioning running, opening a window makes the system stop conditioning that space, and restore it automatically on closing.
3. Air handling unit setpoints on hourly weather. The air handling units do not run at a fixed setpoint but on a value recalculated hour by hour from the forecast, pre-conditioning ahead of the expected load.
4. Domestic hot water at night. It was kept at 100 per cent at all times, including overnight. Hourly modulation was introduced on the zero-demand windows, within anti-legionella requirements.
5. The canteen on usage windows. The air handling units and hot water serving the canteen are turned down or off outside service hours.
6. Generation on a dynamic setpoint. Chillers and boilers run on a sliding setpoint driven by hourly forecasts. Reducing the required temperature lift improves efficiency exactly where the largest share of energy sits.
Why automation is needed
The fair question is whether the same result could be had with procedures and common sense, without a system. The answer is no, and the reason has nothing to do with how competent the staff are.
An operator cannot read the data for every space every hour and decide the next hour’s setpoint. An automatic system can. And the difference is not speed: it is the direction of the control. You do not chase the current temperature, you anticipate the forecast load. Anticipating lowers production peaks, because generation works gradually instead of running after demand. That, repeated hour by hour across every system, is what produces a structural saving rather than an occasional one.
How it lands on the P&L
In Italian residential social and health care the average gross operating margin is 9.3 per cent of revenue, according to ISTAT’s Risultati economici delle imprese 2023.
An avoided cost carries no associated variable costs: it is not revenue to be produced, it is spending that stops going out, so it lands directly on the margin. Against a margin of that order, an avoided cost of the size measured here is not a side item.
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Sources and disclaimer
Results measured at two residential care facilities run by Sintropy, referred to as Facility A and Facility B: the figures are real, the clients are not identified. Method: IPMVP Option C (whole-facility measurement), with climate normalisation on heating degree days at an 18 °C base and price normalisation, over a complete annual cycle. The two-month figures are rounded; the certified data are the annual average, the avoided cost and the winter peak. Sector reference: ISTAT, Risultati economici delle imprese 2023, gross operating margin on revenue for residential social and health care. Informational document: the results are not a forecast for other facilities.