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

Authors: null Chengwei Zhang; null Ziguo Yang;

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

Abstract

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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