Importing hourly resolved data #question
I'm having a question about how to best import hourly resolved timeseries, e.g. for energy consumption of different sectors. My goal is to read the data, be able to aggregate each timeseries for one year and be able to filter data for specific hours, e.g. for one week (hours 1 ... 168).
By now I imported the data following the style in the Tutorial "Aggregating subannual timeseries data", i.e. I used a column "subannual" with values 1 ... 8760 for each hour in one year. Aggregating the timeseries to a year value works perfectly as expected with aggregate_time(). However, the filter()-method doesn't support filtering 'subannual'-data like df.filter('subannual'=range(1, 168+1)). In the documentary however I saw that filtering by datetime is possible.
How would I import the timeseries in a way that filtering is possible? Would I need to put the hour-information in a time-column instead of a year and subannual-column? Does aggregate_time then still work?
Otherwise, could you add the possibility to filter by the column 'subannual'?
I hope to hear from you and I would be very pleased if you add a small tutorial about importing hourly resolved timeseries on your Website.
Patrick Jürgens, M.Sc.
Energy Systems and Energy Economics
Energy System Analysis
Fraunhofer Institute for Solar Energy Systems ISE
Heidenhofstraße 2, 79110 Freiburg, Germany
Phone +49 761 4588-2292
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