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Power system typical load profiles using a new pattern recognition methodology
| Content Provider | Semantic Scholar |
|---|---|
| Author | Tsekouras, George J. Kanellos, Fotis D. Kontargyri, Vassiliki T. Karanasiou, E. S. Salis, A. D. Mastorakis, Nikos E. |
| Copyright Year | 2008 |
| Abstract | In this paper a new pattern recognition methodology is described for the classification of the daily chronological load curves of power systems, in order to estimate their respective representative daily load profiles. It is based on pattern recognition methods, such as k-means, fuzzy k-means and hierarchical clustering, which are properly adapted. The parameters of each clustering method are properly selected by an optimization process using the ratio of within cluster sum of squares to between cluster variation (WCBCR) as an adequacy measure. This methodology is applied for the Greek power system, from which is proved that the separation between work days and non-work days for each season is not so enough descriptive. |
| Starting Page | 25 |
| Ending Page | 31 |
| Page Count | 7 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://users.ntua.gr/vkont/CSCC2008.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
