Segmentation Functionality#

Descriptions of the basic functions are given below.

Function descriptions:

segmentation.segmentation(normalizedTypicalPeriods, noSegments, timeStepsPerPeriod, representationMethod=None, representationDict=None, distributionPeriodWise=True)[source]#

Agglomerative clustering of adjacent time steps within a set of typical periods in order to further reduce the temporal resolution within typical periods and to further reduce complexity of input data.

Parameters:

  • normalizedTypicalPeriods (pandas DataFrame) – MultiIndex DataFrame containing the typical periods as first index, the time steps within the periods as second index and the attributes as columns.

  • noSegments (integer) – Number of segments in which the typical periods should be subdivided - equivalent to the number of inner-period clusters.

  • timeStepsPerPeriod (integer) – Number of time steps per period

Returns:

  • segmentedNormalizedTypicalPeriods (pandas DataFrame) – MultiIndex DataFrame similar to normalizedTypicalPeriods but with segments instead of time steps. Moreover, two additional index levels define the length of each segment and the time step index at which each segment starts.

  • predictedSegmentedNormalizedTypicalPeriods (pandas DataFrame) – MultiIndex DataFrame with the same shape of normalizedTypicalPeriods, but with overwritten values derived from segmentation used for prediction of the original periods and accuracy indicators.