![]() Interpolate the measurement points as trapezoids and integrate them. These things are common in households, so beware of statements like the power flow is stable - it probably isnt :) Capacitive (eg dc power supplies) and inductive loads (any rotating machine) cause harmonics and phase shifts as do loads with duty cycles where the load switches on and off within the supply cycle e.g. There are some devices, phasor measurement units for example, that provide accurate apparent power measurements that reveal how dynamic the loads we typically use are. $$E_\text\right)$$Īll of the above give you the average power flow and not the instantaneous power flow. If you can only take measurement at discrete times, then summing up and dividing by the time between measurements is the only way possible – the integral I'm assuming (again, calc was quite a while ago) that I'd use the trapz version, since the others appear to need a function, and trapz (and simps?) appear to work on sample-style data. ![]() Is the integration version that much more accurate? If I were trying to calculate the kWh over time for my house, and match it as close as possible to what the power company bills me for at the end of the month, what would be best? Is the average * time good enough?įWIW, I'm in python, and looking at. Unfortunately, most of my calc has left me behind, and I'm not sure what type of integration to do over a set of points that doesn't really have any rhyme or reason (ie, f(x) is not just x^2, it's kind of all over the place). ![]() That's the crude version, then calculus is mentioned. I have a system that reads instantaneous watts values, and stores them as "events", with a power value and a time value.Īccording to the answer here ( Should I multiply by time to determine Watt-hours?) it's simply an "take an average of the power readings, then multiply by time" to get it. ![]() This is probably an elementary question, but I wanted to be sure. ![]()
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