Improve the temperature differential by applying a feedback loop
Every house is different. Boilers and plumbing is different. This leads to the temperature rising gradient being specific to each installation. So far so good?
Now we often find a situation where a tado rad stat or room stat is set for, say, 18 degrees cent, but one later finds that the actual temperature is higher. It could be that the boiler overran or the thermostat cut demand late.
How about having a learning loop where if the final temp actually achieved is higher than the set temp, the differential curve is cut back to cut off demand earlier, and if that didn't work, trim again in the next time the thermostat requests heat.
All this is implied in the app, yet I don't see evidence of it happening. Want to try harder guys with the algorithm?
Now we often find a situation where a tado rad stat or room stat is set for, say, 18 degrees cent, but one later finds that the actual temperature is higher. It could be that the boiler overran or the thermostat cut demand late.
How about having a learning loop where if the final temp actually achieved is higher than the set temp, the differential curve is cut back to cut off demand earlier, and if that didn't work, trim again in the next time the thermostat requests heat.
All this is implied in the app, yet I don't see evidence of it happening. Want to try harder guys with the algorithm?
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