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Mitsubishi Ecodan Stats Reporting - Accuracy Worsens In Auto-Adaptation Mode

Sheriff Fatman · 2026-02-10T02:26:36Z

I'm posting this as a heads up for Ecodan users using the Mitsubishi's internal stat reporting to diagnose issues with their system, as it's something that I've discovered 'accidentally' while reviewing other aspects of my system performance, but is something that will potentially lead to mis-diagnosis if it's more widespread than in my own system.I've discovered that the accuracy of the Ecodan's own daily reporting of energy use, which is of questionable accuracy to begin with, significantly deteriorates when the system is running in Mitsubishi's own Auto-Adaptation mode, relative to when it is being run in other modes, such as weather compensation, or constant flow temp mode (which is the mode used by Havenwise and MelPump to control the system remotely, if set up to use in this way). This is essentially masking the performance improvements which running in Auto-Adaptation mode appears to deliver, and which are very apparent from the 'eyeball test' of the daily performance profile.First, a bit of background as to the 'accidental' nature of the discovery. I've been doing extensive monitoring of my 10kW system since installation in June 2025 to better understand a very modest COP performance to date of around 3.0. Initially, I ran the system via Havenwise, and more recently had been using weather compensation. I've been using MelPump and the optional dongle to monitor performance of the system in detail and was very excited via the release of MelPump's own multi-point weather compensation curve setting, which potentially would allow this to be fine-tuned further. Unfortunately, the initial release of the MelPump curve had a bug in it, which caused the curve settings to reset each time they were saved. As a bit of an experiment while awaiting this to be fixed (which I believe was done after a few days) I switched the system to Auto-Adaptation mode out of intrigue. The first time I'd attempted to do this lasted about 30 minutes, as the response had been for the system to immediately increase the flow temp to circa 48°C from something in the mid-30s, but I had been subsequently re-assured by @f1p suggesting that this was part of the self-learning operation it does when the settings are switched. I was also intrigued by some of @f1p's other posts on the forum which suggest that the tolerances used by the Ecodan in Auto-Adapt are different to those available in other modes, suggesting that this mode opens up efficiencies that are not readily available in the other modes. I was intrigued to see if this was noticeable over a short-period of a few days, pending the fix to the MelPump curve settings going live.It took less than 48 hours to realise that the daily profile of operation was noticeably improved within Auto-Adapt. The system was able to deliver much longer periods of sustained running without cycling relative to performance under similar conditions under weather compensation. Interestingly, many of these periods of 'equilibrium' operation showed it to be matching the target temperature to the return temperature, rather than the flow temperature, which is something that seems to be unique to AA mode and which, for some reason, allows the pump to run in a very stable way for extended periods of time. I'm not currently sure why this should be the case, but it seems to work effectively.However, what also became noticeable was that the daily reporting figures from the Ecodan, which MelPump uses within its daily summaries, including the COP calculations, were noticeably different to what was being observed via the 5 minute updates throughout the day. In addition to using MelPump analysis, I have configured the system within Home Assistant to report using the Open Energy Monitor integration, and have been comparing both outputs against each other since early December. Prior to switching to Auto-Adapt there hadn't been a hugely significant variation between the two, or at least to the extent that the differences couldn't be attributed to anything other than an expected margin of error between the two systems. A noticeable divergence between the two systems coincided pretty much immediately with the switch to Auto-Adapt mode.To summarise the type of variance I'm talking about, I've used data from 5th February in the images below. This day has been the 'cleanest' full day of operation I've seen from the system, with almost 24 hours of equilibrium running being interrupted only by a couple of defrost cycles and 3 short periods of DHW heating. The summary from emoncms (the Home Assistant integration) for the day is shown below, in simplified form (i.e. without lines for flow rate, DHW temp and instantaneous COP which I normally have showing): As can be seen, the profile during heating operation (the yellow background) is about as good as it gets in terms of stable running, and the heating COP of 2.94 at an average outside air temperature of 3.1°C (which is low for many people) would be 'acceptable' f...

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Ecoste

(@ecoste)

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23/03/2026 5:50 pm

@sheriff-fatman Thank you for the additional information. Your system seems to be very similar to mine and it produces very similar results. I have used the data from the Ecodan system as my measure of performance, this is provided by Mitsubishi and is their data not something that I have put together. The data for the heat pump specifies a COP of about 5 at 7C, you and I are achieving about 3, that's a big difference. I don't think that there is anything about routing the pipework that should impact the COP. I have had 4 visits from Mitsubishi, they have accepted that the COP is poor and have noted that the data provided is for specific laboratory conditions which are not met by a standard installation.

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cathodeRay

(@cathoderay)

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23/03/2026 8:36 pm

Posted by: @sheriff-fatman

↑

Whether or not it turns out to be statistically significant is another matter, but it's a good visual to show the difference between the two operating modes that caused me to post the findings.

...

I hadn't really studied the above graphs when I posted them, but the data within them is actually quite interesting.

That's why I like charts. The human brain can assimilate information very quickly from images. As I mentioned earlier, the easiest way to check whether the samples are statistically different is to plot the 95% confidence intervals for the regression line and checking whether they overlap or not. This can be done both in R and in python. Here's my infamous 2025 setback vs no setback energy in vs OAT data plotted in R with 95% confidence intervals:

It appears to show the daily energy use is significantly lower when running a setback, at least at lower OATS, but I think the data may be biased. For a start, spring is not the same as Autumn and winter. Other unknown unknowns could also be in play. This in turn led me to the idea of using 'matched pairs' ie match setback days to non-setback days where the variables we do know about are matched. For a start, that means from the same season. I already have the Spring 2025 data, which was a period of setback running, and I am still collecting this Spring's data, which is a period on non-setback running. Once I have all the data (around about the end of April) I will do the plots and see what appears.

Midea 14kW (for now...) ASHP heating both building and DHW

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