ABSTRACT
This paper tackles high computational complexity in using Euclidean distance for residential load profiles (RLPs) similarity by proposing a three-stage incremental segmented slope clustering framework. The first two stages involve static clustering, where we obtain typical residential load profiles through piecewise slope clustering. In the third stage, dynamic clustering is performed based on the slope similarity of RLPs. This method enhances clustering performance and reduces computation cost, outperforming various benchmarks, with simulation results confirming the framework's effectiveness.
Nomenclature
= | a metric that evaluates if the slope aspect of on day and day at time is identical or not | |
= | the average deviation of from the minimum value of each column | |
= | the average deviation of from the minimum value of each column | |
= | the -th clustering centre obtained after clustering user in the first stage | |
= | the final TRLPs of all customers in the static data set | |
= | the slope direction of the -th segment of | |
= | the deviation between and its maximum value | |
= | the deviation of an element in from its minimum value | |
= | the deviation of an element in from its minimum value | |
= | segmented slope co-directional matrix | |
= | the number of slope segments with the same direction on day and day of | |
= | the number of segmented slopes in the same direction on the -th and -th days of the user | |
= | the number of categories after the first stage clustering | |
= | the slope steepness of the -th segment of | |
= | the average slope difference between the RLP and the clustering centre in different slope direction section | |
= | the average slope difference between the RLP and the clustering centre in the same slope direction section | |
= | the average slope dissimilarity between other curves and | |
= | the average slope similarity between other curves and | |
= | the RLPs of user on the -th day | |
= | the number of current TRLPs | |
DBI | = | Davidson-Boding Index |
ISSC | = | Incremental Segmented Slope Clustering |
RLP | = | Residential Load Profile |
SOM | = | Self-Organizing Map |
SSC | = | Segmented Slope Clustering |
TRLP | = | Typical Residential Load Profile |
WSOM | = | Weighted Self-Organizing Map |
Disclosure statement
No potential conflict of interest was reported by the authors.