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I have data of intraday electricity consumptions (by half hours - 48 a day) over a year of 4000 households. Task is to establish baseline consumption of each of these households - possibly also differentiated on seasonality.

One way how to do this would be just taking the mean of the consumption signals. What would be more sophisticated method for this?

I would be very grateful for pointing out to methods I could look into.

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  • $\begingroup$ "Seasonality" not only refers to yearly patterns, but also e.g. the typical day-night baseline change. Look into classic statistical literature for this. $\endgroup$ Mar 19, 2016 at 6:49

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You can try and model your data as having trend/seasonal/cyclic components if you want something a little more sophisticated. Here is an intro reference to get you started.

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I don't think there is need for very complex methods here, I would take a look at several granularity levels and take means for these levels. One important thing is if all the households are in the same area/timezone. After establishing this I would first take the general mean and see per household how they compare to this, then do this for every halfhour segment of the day to get rid of time-of-day seasonality, do this for day of the week, month of the year etc. It really depends on what you want to know what to do with this data.

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  • $\begingroup$ Thank you for your helpful comments. I didn't mention I also have information on things like area of the household and basic income of the owner. Could these be maybe used for more detailed clustering and then creating energy consumption baselines ? $\endgroup$
    – Simon
    Mar 18, 2016 at 11:43

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