...
- validationRules. It is possible to define a set of validationRules. With these validation rules it is possible to define a set of criteria which determines the outputflag of the calculated value based on the number of missing values and/or doubtfuls values counted in the input values.
Output
- outputVariable.
Please note that that the timestep of the output variable needs to have the same "hour" value. E.g. aggregation from daily to monthly timestep works when both are defined for 00:00 GMT, but not when one is set of 08:00 GMT and the other for 00:00 GMT. This typically is only an issue with user-defined timesteps. The default timesteps of Delft-FEWS (or "units") for day, week and year all have a timestamp at midnight.
Description
This transformation performs an aggregation from an instantaneous time series to an aggregated time series. This procedure sums the values of the input timeseries that are within the aggregation period. If no aggregation period is configured, then the aggregation period is equal to the period between the current output time and the previous output time. Alternatively the aggregation period can be configured in the time series set of the output variable. In that case the aggregation period is relative to the current output time and aggregation periods for different output times are allowed to overlap. Using overlapping aggregation periods it is possible to use this transformation to calculate a moving sum. If one of the input values is missing or unreliable the output is missing.
The table below shows an example of accumulating 6-hourly values to daily values using this transformation.
| Original series | Result |
Date/Time | Value | Value |
01-01-2007 00:00 | 1.00 |
|
01-01-2007 06:00 | 2.00 |
|
01-01-2007 12:00 | 3.00 |
|
01-01-2007 18:00 | 4.00 |
|
02-01-2007 00:00 | 5.00 | 14.00 |
02-01-2007 06:00 | 6.00 |
|
02-01-2007 12:00 | NaN |
|
02-01-2007 18:00 | 8.00 |
|
03-01-2007 00:00 | 9.00 | NaN |
03-01-2007 06:00 | 10.00 |
|
The figure below shows original 15 minute data and the aggregated hourly data using the accumulative function:
...