Improved fuzzy clustering algorithm in Long-Term load forecasting of power system

There are some drawbacks of the classical fuzzy clustering algorithm as follow: Firstly, the computing of independent variable weights is unreasonable. Secondly, the set of horizontal section members is slurred. Thirdly, the correlation factor's computational methods are sigular. As to compensa...

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Veröffentlicht in:2010 3rd IEEE International Conference on Computer Science and Information Technology Jg. 9; S. 556 - 560
Hauptverfasser: Chengwei Zhang, Ziguo Yang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2010
Schlagworte:
association analysis
Chaos
Clustering algorithms
Economic indicators
fuzzy clustering
Long-Term load forecasting
Prediction algorithms
ISBN:9781424455379, 1424455375
Online-Zugang:Volltext
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Zusammenfassung:There are some drawbacks of the classical fuzzy clustering algorithm as follow: Firstly, the computing of independent variable weights is unreasonable. Secondly, the set of horizontal section members is slurred. Thirdly, the correlation factor's computational methods are sigular. As to compensate for these aforementioned drawbacks, a new algorithm named improved fuzzy clustering algorithm is improved in this essay. The new algorithm uses association analysis to compute the independent variable weights, sets up a method warehouse and uses it to calculation the correlation factors, and selects distinct members of the equivalent matrix as the set of horizontal section. The demonstration indicates that the new algorithm increased the accuracy of forecasting result.
ISBN:9781424455379
1424455375
DOI:10.1109/ICCSIT.2010.5563614