Actually, on second thought, it's the actual temperatures which are measured and reported as integers. Given that those do vary, and given that flow rate is roughly constant and thus dt varies, the average dt should have better resolution.
So I don't think that its likely to explain the discrepancy between 8kW measured and 11kW specified.
At the moment, it seems to me there is not enough second thoughts going on, and instead there is a lot of noise repeating stuff we already know - in some cases have known for years - and the signal is getting lost. I for one don't need to be told yet again that PHEs interfere with heat transfer, or that Midea claim my heat pump can deliver outputs around 11.5kW at high LWTs and low OATs, or that accurate measurements are difficult and expensive to do, but conversely if you want delta t rather than absolute values then the errors may cancel out leaving you with not too bad an estimate of the delta t, and generally Midea's habit of using integers is a pain in the arse. All of this and more has been known for ages. Merely repeating it is just creates more noise without a signal.
What we are interested in here is the practical question of whether setbacks can save energy without compromising comfort. That has led to some secondary questions like what is the real OAT, and what is the real heat pump output. On the face of it there are some pretty serious discrepancies on this latter question: 8kW max output from a 14kW unit might well be enough of a discrepancy to put a sparkle in the eye of a local trading standards officer.
Having looked again at the graph I'm not sure I agree with your analysis. The lwt achieved appears to be equal to the lwt set, so the heat pump appears to be doing what you are asking it to do, rather than failing to do so due eg to lack of capacity.
Are you sure that the lwt set is high enough for your emitters to deliver the required output? Graph suggests not, and heat pump can only supply what the system demands.
Well, I am not sure I agree with your analysis. When the actual LWT is varying all the time, it is not the peaks that reflect the actual LWT over time, but the average. Here's the chart again:
On the left, during the defrost cycles, the actual LWT only briefly touches the set LWT some of the time. The average actual LWT over time is less, considerably less, and can be calculated from the raw data (though I haven't done that, the Mk 2 Eyeball Integrator is good enough for me here). On the right, where there is 'normal' cycling, the average actual LWT is close to the set LWT. I think it is possible Midea know their heat pumps almost always cycle in normal use, and so have the control logic set up to achieve an average actual LWT that matches the set LWT. Even if they don't, that is what appears to happen in practice during 'normal' cycling. When defrost cycles appear, all that goes out of the window. I suspect the average actual LWT on the left during the defrost cycles may in fact be a bit less that on the right hand side, during the 'normal' cycles. The heat pump is not doing what is being asked of it, instead, it seems heat pumps, at least my heat pump, have/has a serious flaw built in, it in effect fails when it is needed most. You can see that happening in the chart. The average LWT (and so ultimately the energy transfer, because of the steady flow rate) on the left is the same or a bit less than that on the right, despite the fact that on the left the reported OAT is a whole 8 degrees less.
Assuming the Midea reported modbus collected raw data is tolerably accurate - and it probably is, last time I checked, the calculated energy out was close to that based on the Midea total lifetime energy out: for the last 24h, calculated energy out is 133.71, that based in the lifetime energy out is 133 - then the burning - or perhaps that should be failing to burn - question is why does my heat pump only deliver ~8kW max power when on paper it is supposed to be capable of ~11.5kW power? What appears to be happening is that it delivers at the 'norm' capacity level (CL - see table below, zero OAT, Set LWT 50 = 6.6 - 11.7kW out). Of course, the actual energy delivered to the interior of the house is even worse. For me, the question is why is it apparently delivering at the 'norm' capacity level when it needs to deliver at nearer the 'max' capacity level? Raising the Set LWT isn't the answer, it is already all but maxed out, and in fact raising it to 60 degrees actually reduces the power output (COP goes through the floor when LWT goes above 55, 'norm' capacity level at zero OAT falls from 8.8kW at LWT 50 to 7.2kW at LWT 60).
The chart (the graph, not the table) above also suggests that the 'real' OAT is not 'several degrees' above the reported OAT. Since the real OAT is unknown, strictly speaking there is no way of knowing what the difference is. However, when we look at the right hand side of the chart, the recorded OAT only rises by one degree during the periods when the compressor is off. That is not 'several degrees'. The picture on the left hand side is complicated by the defrost cycles, during which heat from the house is actively and directly delivered to the heat pump. Of course the apparent OAT goes up, because the OAT sensor is inside the heat pump which is being heated to melt the ice. Those spikes do not tell us anything about the real OAT.
Your data on the graph is consolidated into hours. Do you have any more granular data that will show whether your ASHP ever outputs 11 or so kW? Given the detailed anatomy of a defrost, is it possible that it does get that high but the average over an hour is less? If you look at this close up from mine, an hour sample is going to have an average much lower than the peak. (200 on the vertical axis is 12kW). As well as the fact it stops for 7 or 8 minutes and then has to reheat the flow, it looks like the COP gets worse leading up to the defrost. BTW, I assume it's limiting the steady power out to about 10kW because I've limited my flow (to 43 deg).
Some ASHPs do quote figures incorporating defrosts but I suspect they are based on what the BS standard requires rather than a realistic allowance for a UK winter. I have always said you should add on 15-20% to your calculated design heat loss to allow for defrosts.
I do think the advantage of a setback (and I'm convinced that for me there is one) will be diminished when defrosts are taken into account, especially when the ASHP really struggles to recover.
I’m a bit lost on this thread can someone enlighten me if the topic has changed? It was :
“Do setbacks save energy without compromising comfort?”
I agree, and every now and then I as the thread starter try to remind folks what this thread is actually meant to be about (eg an hour or so ago I wrote: "What we are interested in here is the practical question of whether setbacks can save energy without compromising comfort"). This objective does get regularly compromised by certain individual(s) who shall remain anonymous in this particular post who like to gloat over point out the deficiencies in my heat pump system, despite the fact the said deficiencies have been established and then beaten to death countless times over the last couple of years. Apart from remaining as a background caveat (my system is not necessarily a typical system), they do not need to be constantly repeated.
There is also a direct clash between the empirical approach (me) and the modelled approach (@derek-m), with I think it is fair to say @jamespa sitting somewhere in the middle, but leaning perhaps a bit more towards modelling. I suspect this is due to our backgrounds: I am totally familiar with using messy real world data to make pragmatic decisions (medicine/epidemiology), whereas the other two come from engineering/physics backgrounds where models and precision are greatly valued. Both approaches of course have value.
I also take the view that even if my system isn't typical, it still remains the case that I (and @kev-m) are the only ones who have collected and published comprehensive raw minute by minute data as well as many charts derived from the data, meaning that this is the only data we (as a group) have to work with - currently it is my/@kev-m's raw data or none - and furthermore, while the specifics of my system may not be typical, it might nonetheless be able to answer the general and pragmatic real world question posed in the thread title. In other words, having a PHE doesn't mean I can't answer the question in the thread title. The specifics may be different, but the overall answer might be general.
I am sorry that you appear to have misinterpreted what was meant to be friendly, helpful, advice, as a personal attack on yourself.
I am a firm believer in collecting and using real World data from all necessary sources, so that meaningful analysis can be made to arrive at the correct conclusions. Obtaining, collecting, and displaying precise, accurate, data has been one of my duties for over 50 years on complex industrial systems, so I would hope that by now I am fully aware of the strengths and weaknesses that data collection can present.
If I question the accuracy of some data points, it is not out of some form of vindictive spite, but merely to confirm that the data being obtained is as close to accurate as possible, or to point out possible causes of inaccuracies, so that allowance may need to be made when the data is subsequently used.
I don't get some perverse delight in pointing out that a system, or equipment, is operating in a sub-optimal manner, but would hope that the owner would welcome such knowledge, so that corrective action can be taken.
At the end of the day please feel free to operate your heating system in whatever manner you feel is best. I will endeavor not to post anything that you may find upsetting, but it may be best if you don't read my posts if you find them so annoying.
@jamespa - interesting. The confidence interval overlap is indeed very small, and may or may not disappear with larger sample sizes. At the moment the (non-significant, by a whisker) difference is not trivial, at about 20%.
For the umpteenth time, we all know very well that strictly speaking data from X only applies to X. It is a basic tenet of science. The question we can very legitimately ask ourselves is to what extent can we generalise from that data? What is reasonable and what is not? We do this in the real world, at least in the medical world, because we never have the data for an entire population (and, it has to be said, often have very far from perfect experimental data), and even is we did have complete data, we still probably don't know for sure where you, as an individual, are in that population. Bear in mind that when a doctor makes a strictly evidence based decision about what treatment to recommend to you, that doctor has no way of knowing whether it actually applies to you. Instead, he or she has generalised from the research, which is always constrained, to you, a unique individual who may well have some characteristic that means the research doesn't apply to you. If the doctor also knows his or her numbers, he or she will also know, rather frustratingly, that in most cases the recommended treatment won't work - but that another story for another day. Google 'NNTs' to get some understanding of why this is the case, particularly for the mass market medications that are in such common use these days.
The point that I am starining to make is that in the real world with very variable often unique subjects (people, houses) we have no alternative but to work with - do our best with - noisy messy imperfect data. Even worse, our data may just be plain wrong - medicine is full of examples. If we can't tolerate that (and manage that state of affairs), then we may as well stay at home in bed all day, watching day-time TV with the curtains drawn.
Back to the question in hand here in this thread: is having a PHE likely to alter the result in a way that matters? Sure it very likely will alter the individual (setback vs non-setback) results, but if they both move up or down by the same amount, then the actual answer we want (is there a difference in energy use) is unaffected (similar to the absolute LWT/RWT vs delta t thing).
We can even do some lightweight whatiffery modelling: what if the building thermal mass is set to a different value? - in other words, do a sensitivity analysis. Does it or doesn't it make much difference?
Ditto for different OAT ranges - and whatever other factors you may wish to consider. If they just move the absolute positions of the results this way or that, but the relative difference stays the same, then that factor doesn't matter for the question at hand.
For the umpteenth time, this is the only data we have: it's either this data, or nothing, unless and until someone else produces comparable long term data.
Midea 14kW (for now...) ASHP heating both building and DHW
I am sorry that you appear to have misinterpreted what was meant to be friendly, helpful, advice, as a personal attack on yourself.
I have not taken it as a personal attack on me, instead I am trying emphatically to say that constantly pointing out the obvious increases the noise and gets in the way of the signal. There is no place for or point in ad hominem attacks - I even crossed out gloating over because it seemed a bit of ad hominem from me, and changed it to pointing out (though I do admit to leaving the crossed out bit...).
I have no doubt you appreciate the importance of real world data, what is under discussion here in this thread is the approach to answering the core question: start with a model or start with real world data? They are just two different approaches, and different people will give different weights to their different pros and cons.
Midea 14kW (for now...) ASHP heating both building and DHW
For the umpteenth time, we all know very well that strictly speaking data from X only applies to X. It is a basic tenet of science. The question we can very legitimately ask ourselves is to what extent can we generalise from that data? What is reasonable and what is not? We do this in the real world, at least in the medical world, because we never have the data for an entire population (and, it has to be said, often have very far from perfect experimental data), and even is we did have complete data, we still probably don't know for sure where you, as an individual, are in that population.
Im not actually sure now what you think we disagree over. All I was doing is outlining a process by which we might, using a combination of experimental data, modelling and theory, get to some more general conclusions. Perhaps you think this is so obvious as to not be worth stating (no need to reply to this). Well maybe, but sometimes it helps, at least in my world, to reaffirm assumptions so that people have the opportunity a) to recall them and b) to challenge them if they don't agree.
We can even do some lightweight whatiffery modelling: what if the building thermal mass is set to a different value? - in other words, do a sensitivity analysis. Does it or doesn't it make much difference?
The correlation coefficient doesn't change much if the IAT correction factor varies by +/-4K. The OAT correction factor anyway is a minor effect. I haven't yet looked at the effect on the slope, and wont do until we have enough data to have a statistically significant difference.
I'm currently trying to think about the rather large intercept in the setback condition, which also has a very large uncertainty. This is material to the conclusion, because it has the potential to more than wipe out, at milder temperatures, any 'saving' one might expect based solely on the difference in slope. Without at least a plausible explanation for it, I wouldn't want to apply any conclusion we might reach from experiments to any circumstance other than ones for which we have experimental data. This is a position of little value to anyone other than those who provided the experimental data.
4kW peak of solar PV since 2011; EV and a 1930s house which has been partially renovated to improve its efficiency. 7kW Vaillant heat pump.
@kev-m - I think you may be onto something, an average hiding detail. Here's a zoom in on three defrosts over a two hour period on a recent cold morning:
Now if I calculate the minute by minute energy out, and treat that as if it is the current power out (I think that's legit): I get this for the 08:00 to 08:59 hour:
which shows there are times when the power out approaches, and even just exceeds 10kW - about 10% below the 'max' capacity level - but the average (pulled down further by the negative kW values during the defrost - presumably meaningful as energy is being sucked from the house to warm the coils) is much less, and the corresponding kWh for the hour are much less, 6.71 in this chart, 7.06 in the upper chart right hand bars (can't explain the discrepancy).
I'm still stuck with the fact the building is losing approx 11kW over an hour at zero degrees (ie 11kWh), while the heat pump is only adding around 7kWh during that hour.
Midea 14kW (for now...) ASHP heating both building and DHW
@jamespa - I am continuing to collect data, which will only apply to my compromised ungeneralisable setup in the conditions in which it was collected, and when the stars are in the same alignment as they were at the time of the data collection, and I maintain a rigidly fixed daily routine, so as to avoid introducing tiresome human factors into the equation.
It already seems pretty apparent to me that setbacks are not a good idea in cold OATs, say 5 degrees or less, but when it is milder I will try running with setbacks again for a while, possibly with a slightly more aggressive recovery boost eg add two or perhaps three degrees to Set LWT when IAT is one or more degrees below desired IAT, which will I know lower COP during the recovery, but it should be faster (to meet the comfort requirement) and being faster will not last as long. Or perhaps I should keep the recovery boost as it is, to avoid introducing yet another variable into my compromised ungeneralisable setup in which the data only applies in the conditions in which it was collected, and when the stars are in the same alignment as they were at the time of the data collection, and I maintain a rigidly fixed daily routine, so as to avoid introducing tiresome human factors into the equation.
Midea 14kW (for now...) ASHP heating both building and DHW
I am sorry that you appear to have misinterpreted what was meant to be friendly, helpful, advice, as a personal attack on yourself.
I have not taken it as a personal attack on me, instead I am trying emphatically to say that constantly pointing out the obvious increases the noise and gets in the way of the signal. There is no place for or point in ad hominem attacks - I even crossed out gloating over because it seemed a bit of ad hominem from me, and changed it to pointing out (though I do admit to leaving the crossed out bit...).
I have no doubt you appreciate the importance of real world data, what is under discussion here in this thread is the approach to answering the core question: start with a model or start with real world data? They are just two different approaches, and different people will give different weights to their different pros and cons.
I have no desire to get into a slanging match with you, since it would most likely be counter-productive.
If, from time to time, I refer to some of the known weaknesses in the design, installation and operation of your system, it will have been in the context of answering a particular question or clarifying a point in a particular post.
I hope that you would agree that most data is not accurate, but data can still be used productively if the inaccuracies are known, and accommodation can be made in any
subsequent use of the data. Obviously this becomes problematic if the inaccuracies are not known, and instead unsupported assumptions are used instead.
The reason that I may question certain aspects about how your system is operating, and being operated, is to identify any inaccuracies in the raw data being provided, and try to quantify their effect.
The Modeling Tools were developed to help answer questions such as the original one posed by yourself, but the Modeling Tool, just like any mathematical method, will only provide reasonably accurate answers if the data used is itself reasonably accurate.
I don't now who decided to use the word 'noise' to describe inaccuracy. I'm afraid it is not an Engineering term with which I am familiar.
I don't now who decided to use the word 'noise' to describe inaccuracy. I'm afraid it is not an Engineering term with which I am familiar.
Guilty as charged. Its a term used a lot in electrical engineering, less so in mechanical or software engineering. I am using it to describe succinctly both random, and seemingly random, purturbations, some if which we can account for, and many of which we can't or won't account for. I will continue to use it unless a) someone seriously objects and b) someone comes up with a better, and similarly succinct, term.
I don't now who decided to use the word 'noise' to describe inaccuracy. I'm afraid it is not an Engineering term with which I am familiar.
Guilty as charged. Its a term used a lot in electrical engineering, less so in mechanical or software engineering. I am using it to describe succinctly both random, and seemingly random, purturbations, some if which we can account for, and many of which we can't or won't account for. I will continue to use it unless a) someone seriously objects and b) someone comes up with a better, and similarly succinct, term.
Yes, I fully understand noise when used in the electrical/electronic field, but I feel many of the forum members may find this term confusing when used in the context of mathematics and data processing.
I don't now who decided to use the word 'noise' to describe inaccuracy. I'm afraid it is not an Engineering term with which I am familiar.
Guilty as charged. Its a term used a lot in electrical engineering, less so in mechanical or software engineering. I am using it to describe succinctly both random, and seemingly random, purturbations, some if which we can account for, and many of which we can't or won't account for. I will continue to use it unless a) someone seriously objects and b) someone comes up with a better, and similarly succinct, term.
Yes, I fully understand noise when used in the electrical/electronic field, but I feel many of the forum members may find this term confusing when used in the context of mathematics and data processing.
I think many may find quite a few terms used on the forum from time to time confusing.
As i say I'm happy to consider another succinct term to describe the seemingly random spread in experimental measurements, but until such a term emerges will continue to use 'noise'. Perhaps we need a glossary of terms used on the forum?
4kW peak of solar PV since 2011; EV and a 1930s house which has been partially renovated to improve its efficiency. 7kW Vaillant heat pump.
Here are the results of running the Libreoffice regression analysis on two subsets of the data, corresponding to when there was and when there was not setback.
.... Also note the very high intercept in the setback data. This must mean something, I don't know what, and if the value is maintained when we have more data it definitely matters to the answer to the question posed in the thread!
@cathoderay, @derek-m I've been thinking about the possible origin of the high intercept when setback is operational, much larger than when setback is not operational.
In terms of the correlation this is the heat energy which needs to be put in (when setback is operational) in the theoretical scenario where there are zero degree-minutes (ie the house doesn't need to be heated). Obviously one wouldn't be operating the heating at all in this scenario, but it should nevertheless help to think about it this way as an aid to identifying the likely underlying physical mechanisms. So we are looking for something in the real system which requires energy largely independent of degree minutes, but which only occurs when setback operates.
The intercept in the @cathoderay data is between 9.3 and 28.1kWh (95% confidence) with a central value of 18.7kWh. I now think that the most likely explanation (and almost certainly a part of the explanation) is the heat stored in the heating system itself. If you think about it, in a setback scenario this heat is lost (largely to the house) during setback and has to be recovered after setback, and the amount of energy needed for the recovery is roughly the same irrespective of OAT (it does varies with OAT if WC is operational, but even at the highest OATs is still a significant quantity of energy). The energy must be supplied to the system during recovery, but has not been lost altogether because it was originally leaked to the house. Thus its effect (in terms of the plot) will be to introduce a floor (intercept) to the energy demand vs degree minutes curve, but to reduce the slope thereafter, which is what we see. From the householder point of view it will reduce the rate at which the house cools and increase the recovery time (because the heating system itself needs to get up to temperature).
When setback is not operating the heating system never cools completely, probably explaining the very different intercepts.
I don't know what the volume of @cathhoderay 's system is, but 18.7kWh at a deltaT of say 25C requires 625l of glycol (shc = 4100j/kgC), probably rather more than @cathoderay has. But of course the 'heating system' includes the heat pump itself, material immediately surrounding the pipework, and the emitters, so its not so far out as to be implausible.
@derek-m whether or not this is the whole explanation it certainly is a significant observation from the data which could, in principle, be fed back to the model. Personally I am unconvinced that its worthwhile but you may think otherwise! Either way this is another finding from the data which will almost certainly apply to all systems.
4kW peak of solar PV since 2011; EV and a 1930s house which has been partially renovated to improve its efficiency. 7kW Vaillant heat pump.
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