I’ve actually come up with a new optimization dimension to combine the above algorithm with I’d like
to hear your opinion on.
Thing is, efficiency of air-to-water (or air-to-X) heat pumps very much depends on outside air temperature. In short, when you heat during the coldest hour right before sunrise it’s say -10°C and noon that same day, with sun shining, temps can well rise to say +5°C, and note commonly provide temp values are measured in the shadow, in sunlight it can be even more, also in winter.
That 15°K spread can well make up for a 30% increase in efficiency. That’s potentially even more savings than what can be achieved through selecting cheapest hours.
While not intuitive it’s in fact clever to NOT heat when it’s coldest in the morning (and most expensive) and to heat around noon (when on average it’s both, warmer so more efficient AND cheaper).
There’s conflicts however in the evening (expensive once more but still somewhat warm) and even more so at night (cold so ineffective but then again cheap).
So how to handle those ?
I don’t think we can compare efficiency spread to price spread. I’ve spent a fair amount on design thinking but I have not come up with any good common metric I can think of to reliably compute savings and to simply compare to select the best strategy at any point in time.
So my idea was to use the 4(or 3)-level system laid out before (corresponding to SGready modes), plus sort hours by forecasted temp, and suppress heating during the n coldest hours.
Have that take precedence if there’s a conflict i.e. when it’s the normal heat level according to price but inefficient according to temperature (i.e. among the coldest hours), temp will rule and heating is suppressed that hour (n being user configurable again like in the old concept will ensure we don’t suppress too many hours of the day and user has the choice).
Also note the elegance of this: it’s rather simplistic in algorithmic terms and implementation, but more important it’ll work even if you have NO dynamic tariff ! (or if there’s problems obtaining the current price data).
wdyt?