A first quick plot, to see if I am wasting my time or not: all hourly data for the entire period I have data for, 25 March 2023 to date, OAT vs energy in. Note (a) this is a quick ALL data plot, summer included when heating was off, meaning lots of zero energy in points, different WCC settings, meaning we expect some spread and (b) it is calculated space heating energy in only (DHW excluded) but is not corrected for the calculated value under-read which increasingly looks as though it is consistently 80-82% of the correct value. The ONLY question I am trying to answer with this plot is: does there appear to be a relationship between OAT and Energy IN?

It looks to me as though there is, at least it seems sufficiently likely to make it worth teasing out individual periods when other parameters notably the WCC remained constant. 

There is clearly a relationship but the spread of +/-20% ignoring the outliers is of the same order as that I measured on my gas boiler (which was actually degree days Vs daily consumption and thus expected to be and in fact somewhat a bit tighter).

Unless you can tie down the variables to narrow the spread, this is pretty much what my comments about the need for lots were based on, albeit that the noise is perhaps a tad worse.  I would say that there is no chance in a single measurement of spotting a signal which is certainly no more than 10%, and perhaps more like 3-5%.  

You are going to need quite a few measurements and some data processing unless you can tie down the variables to get a tighter curve.  Unfortunately, as @derek-m points out, you can't even use degree days to reduce noise by averaging over 24hrs, because of the variation of cop with oat.

This stuff is just difficult!

Posted by: @jamespa

↑

There is clearly a relationship but the spread of +/-20% ignoring the outliers is of the same order as that I measured on my gas boiler (which was actually degree days Vs daily consumption and thus expected to be and in fact somewhat a bit tighter).

Unless you can tie down the variables to narrow the spread, this is pretty much what my comments about the need for lots were based on, albeit that the noise is perhaps a tad worse.  I would say that there is no chance in a single measurement of spotting a signal which is certainly no more than 10%, and perhaps more like 3-5%.  

You are going to need quite a few measurements and some data processing unless you can tie down the variables to get a tighter curve.  Unfortunately, as @derek-m points out, you can't even use degree days to reduce noise by averaging over 24hrs, because of the variation of cop with oat.

This stuff is just difficult!

Hi James,

There is a relationship between OAT and Energy In, but it is not as clear as the relationship between OAT and Energy Oat.

The relationship is not linear, since it is dependent upon COP, which itself is not linear, but varies with loading.

@jamespa - as I pointed out, I expected spread, because this is all data, as it sits in the data file, and it is just a screening plot. All sorts of things changed during the period, including the WCC, so it would be very surprising if there wasn't spread.

I repeat, the only question I wanted to answer with this plot is: am I wasting my time? Had there been no visible relationship, I would have given up there and then, but there clearly is a relationship, making it worth looking at defined periods when things, especially the WCC end points, were stable. This is going to be a tedious manual process (the changes in the WCC are in hand written notes, and I will have to code them into the data), meaning I didn't want to waste time doing that if it was unlikely to bear fruit.

I think maybe I also need to repeat the point of this whole exercise, which is to determine expected energy IN, given a certain OAT, with no setback, and then compare 24 hour totals of expected energy IN in that running state with 24 hour totals of observed energy IN when there is a setback.  

@derek-m - I did not expect it to be linear, and the screening plot appears to confirm that, the trend curves upwards as the OAT falls.     

Posted by: @derek-m

↑

Hi James,

There is a relationship between OAT and Energy In, but it is not as clear as the relationship between OAT and Energy Oat.

The relationship is not linear, since it is dependent upon COP, which itself is not linear, but varies with loading.

I realise that, but the object here is to be in a position to average sufficient data points to reduce the level of noise below the signal.  Its possible one could do daily averaging factoring in OAT and COP, to get a 'corrected' average energy in over 24 hrs so that one day could be fairly compared with another notwithstanding the differences in OAT.  Not straightforward though to do it properly, but I grant possible.

Posted by: @cathoderay

↑

I repeat, the only question I wanted to answer with this plot is: am I wasting my time? Had there been no visible relationship, I would have given up there and then, but there clearly is a relationship, making it worth looking at defined periods when things, especially the WCC end points, were stable. This is going to be a tedious manual process (the changes in the WCC are in hand written notes, and I will have to code them into the data), meaning I didn't want to waste time doing that if it was unlikely to bear fruit.

I think maybe I also need to repeat the point of this whole exercise, which is to determine expected energy IN, given a certain OAT, with no setback, and then compare 24 hour totals of expected energy IN in that running state with 24 hour totals of observed energy IN when there is a setback.  

Fully understood and appreciated, I was just sharing my thoughts given the information you have newly supplied.  My fear (for you) however is that, even though there clearly is a relationship you may well be still wasting your time sifting through the data, because there are variables on which you wont have notes and anyway cant quantify (solar gain, house usage (eg cooking) and OAT history) that will cause a spread of a similar magnitude and will still result in the conclusion that you need quite a few data points to extract the signal from the noise.  

Personally, having regard to the objective, I would now concentrate my efforts on collecting robust experimental data with and without setback, but leaving everything else the same.  However this is your choice, I could yet be proven wrong and it may be that when you take out the effect of the known variables the data tightens up significantly, its just that I wouldn't place abet on this being the case.

@jamespa

If my understanding is correct, I think that you will find that the modeling tool performs the task that is being requested, since it calculates the Energy In for each 1 hour period of the day based upon OAT and Energy Out.

@jamespa - at this stage, I am only looking at the relationship between energy in and OAT, and as the heat pump in normal WC no auto-adaption mode sets the LWT (and as a result, the energy in) based solely on the OAT, those other factors, which the heat pump knows nothing about, will not affect LWT/energy in.

The problem is I can't collect what you call robust experimental data with the other variables controlled because many of them are beyond my control. I can only collect noisy messy data, but perhaps I am more comfortable with that, because I am used to working with epidemiological data, and that is almost invariably noisy and messy, sometimes almost to the point where the only data worth its salt is mortality data, and then you realise even that is noisy and messy eg the elastic definitions of a covid death during the pandemic meant it was impossible to know how many alleged covid deaths were really covid deaths. You then end up giving up on case definitions, and look solely at excess mortality, only to find that is noisy and messy too! Was the excess due to covid, or anti-covid interventions (shutting down routine healthcare, emergency patients avoiding hospital etc). All I am just saying is that I am very used to, and familiar with, noisy messy data.

For the heating data, I am going to start by finding the longest period of steady WCC endpoints, and then pull out that data and see what it shows. The main problem is the data overall is sparse for lower OATs, which in many ways is the operating conditions we are most interested in. God forbid that I should ever want cold weather, but a bit of me says if only, just to get some data. 

If it looks as though there is something in the data, I will persevere, if not I will move on to something else.         

Posted by: @cathoderay

↑

You then end up giving up on case definitions, and look solely at excess mortality, only to find that is noisy and messy too! Was the excess due to covid, or anti-covid interventions (shutting down routine healthcare, emergency patients avoiding hospital etc). All I am just saying is that I am very used to, and familiar with, noisy messy data.

Good to hear.

By robust I don't mean low noise (I think that's impossible without a lab), as I say and as you doubtless know there are techniques for digging signal out of noise and so what I mean is data which lends itself to the application of these techniques.

The problem should be a bit easier than getting Covid deaths, because there are no ethical problems with deliberately switching on and off the variable whose effect we are trying to assess!

Posted by: @derek-m

↑

If my understanding is correct, I think that you will find that the modeling tool performs the task that is being requested, since it calculates the Energy In for each 1 hour period of the day based upon OAT and Energy Out.

It will not. I am not persuaded you have read my recent posts describing what I am trying to do, or whether you have, and I have written them so badly you failed to grasp what I was saying, but the key point and difference is I start with observed data, not a model. The only 'modelling' involved, and I wouldn't even go so far as to call it a model, is to fit, in effect, a regression line to the OAT/Energy In plot (a process which I would, unsurprisingly, call fitting a regression line), which I can then use to derive expected energy in at various OATs. I don't even need the underlying regression equation, I can instead use the regression line as a sort of look up table. Apart from the regression line, the whole point is not to use a complex model, and instead do a simple transparent and understandable comparison, between expected and observed. Epidemiologists do that sort of thing all the time, excess mortality is just the observed number of deaths compared to the expected number of deaths (and can be negative, if the observed number is less than expected). Having said that, it can also be problematic: how were the expected number of deaths determined? Exactly the same problem applies to my heat pump data, which is why I want a transparent and graspable method that does not involve complex modelling.

Posted by: @jamespa

↑

The problem should be a bit easier than getting Covid deaths, because there are no ethical problems with deliberately switching on and off the variable whose effect we are trying to assess!

It should, the main complication with the covid deaths data is human behaviour, deciding what is and isn't a covid death, and those decisions, particularly with pandemics, are often political decisions, not medical decisions, that make political sense, not medical sense. And then there are things like hot stuff bias (the tendency to be more likely to make a diagnosis when it is a hot topic). At least we by and large don't have those complications to deal with! 

Posted by: @cathoderay

↑

Posted by: @derek-m

↑

If my understanding is correct, I think that you will find that the modeling tool performs the task that is being requested, since it calculates the Energy In for each 1 hour period of the day based upon OAT and Energy Out.

It will not. I am not persuaded you have read my recent posts describing what I am trying to do, or whether you have, and I have written them so badly you failed to grasp what I was saying, but the key point and difference is I start with observed data, not a model. The only 'modelling' involved, and I wouldn't even go so far as to call it a model, is to fit, in effect, a regression line to the OAT/Energy In plot (a process which I would, unsurprisingly, call fitting a regression line), which I can then use to derive expected energy in at various OATs. I don't even need the underlying regression equation, I can instead use the regression line as a sort of look up table. Apart from the regression line, the whole point is not to use a complex model, and instead do a simple transparent and understandable comparison, between expected and observed. Epidemiologists do that sort of thing all the time, excess mortality is just the observed number of deaths compared to the expected number of deaths (and can be negative, if the observed number is less than expected). Having said that, it can also be problematic: how were the expected number of deaths determined? Exactly the same problem applies to my heat pump data, which is why I want a transparent and graspable method that does not involve complex modelling.

Please feel free to ignore all my post.

I've got through the data, and while there were a fair number of WCC end point changes over last winter, they all happened mid winter, and the period from the time I started full data collection and the end of the heating season ran throughout on one WCC setting, with no setbacks (apart from one power cut on April Fool's day). Here is the scatter plot:

The plot has tightened up considerably, especially at higher OATs, and I feel reasonably confident in saying that when the hourly average OAT is for example 10 degrees, then the energy in will be, on average (average is OK, it will even out) 1.2 kWh. I can't explain the higher OAT outliers, always below the main line - could be hangover effects eg a transition from DHW to heating or vice versa, or more likely an hour where some of the energy in went to DHW heating, so that going to space heating was less than it would otherwise have been for that hour eg in a DHW priority heating hour, 30m is spent heating the hot water, 30m on space heating, meaning the total for space heating for that hour will be half what it should have been for the given OAT. I suspect the increasing spread at lower OATs, becoming decidedly more visible at 5 degrees and less, may well be due to defrost cycles, an hour that has more defrosts will presumably use less energy in, because it is not heating the water so much. Overall, the period was longish (a very precise scientific term) and there was a reasonable spread of OATs, but with less points at lower OATs,

The period 16 Oct 23 to 4 Nov 23 also ran with one WCC throughout, and was characterised by mostly mild OATs. Here's the scatter plot:

Perhaps slightly more scatter, but that may be a visual artefact cause by less data points. Nonetheless, again I can say that, given an average hourly OAT of 10 degrees, the average hourly energy in will be 1.1 kWh.

Are these estimates of expected energy in for given OATs good enough? I think they probably are, partly because they are based on real data, and partly because the scatter plots look credible. Personally, I am not too bothered about the spread at a given OAT, provided the spread is reasonably symmetrical (technically, they are close enough to a parametric distribution), which most of the time it is, because the average will then reflect a meaningful number - the average energy in for that OAT.  

Two important caveats: (a) these are data from the csv file, I haven't yet applied the calculated to actual correction factor and (b) the weather curve is now slightly different (both ends a bit lower) than it was during the above two periods, meaning neither of the above apply directly to the current running state, regardless of whether I have setbacks and recovery boosts or not. I can however adjust the current WCC to match that used previously, and then the estimates from the above data will apply, at least well enough to satisfy me. After all, I am not trying to find the Higgs Boson particle, instead, I am just trying to get a pragmatic answer to a simple question with a surprisingly elusive answer.