DocumentCode
1981037
Title
Notice of Retraction
An interval analysis method of information granulation considering load uncertainty
Author
Lili Zeng ; Rui Ma
Author_Institution
Inst. of Electr. & Inf. Eng., Changsha Univ. of Sci. & Technol., Changsha, China
fYear
2011
fDate
16-18 Sept. 2011
Firstpage
3824
Lastpage
3827
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Considering load uncertainty, based on the Rough Set attribute reduction algorithm, this paper is to find the key attributes and then find the most similar data of prediction day through reoccupy Fuzzy C-means clustering algorithms, while the data are as inputs to Fuzzy Information Granulation (FIG), and then, with the data through FCM and FIG´ s as the inputs of least squares support vector machine(LLSVM) to predict the interval load value. Finally the example analysis shows that, this method not only can be effectively used for predicting the uncertainty load interval values of power system, but also provided more abundant information for the planning workers than traditional method optimization theory.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Considering load uncertainty, based on the Rough Set attribute reduction algorithm, this paper is to find the key attributes and then find the most similar data of prediction day through reoccupy Fuzzy C-means clustering algorithms, while the data are as inputs to Fuzzy Information Granulation (FIG), and then, with the data through FCM and FIG´ s as the inputs of least squares support vector machine(LLSVM) to predict the interval load value. Finally the example analysis shows that, this method not only can be effectively used for predicting the uncertainty load interval values of power system, but also provided more abundant information for the planning workers than traditional method optimization theory.
Keywords
fuzzy reasoning; fuzzy set theory; least squares approximations; load forecasting; pattern clustering; power engineering computing; power system planning; support vector machines; FCM; FIG; Fuzzy C-mean clustering algorithms; LLSVM; fuzzy information granulation; information granulation; interval analysis method; interval load value; least squares support vector machine; load forecasting; load uncertainty; optimization theory; planning workers; power system; rough set attribute reduction algorithm; Clustering algorithms; Forecasting; Load forecasting; Load modeling; Prediction algorithms; Uncertainty; Fuzzy C-means(FCM); Fuzzy Information Granulation; Least Squares Support Vector Machine(LLSVM); Rough Set(RS); load interval values; uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical and Control Engineering (ICECE), 2011 International Conference on
Conference_Location
Yichang
Print_ISBN
978-1-4244-8162-0
Type
conf
DOI
10.1109/ICECENG.2011.6057426
Filename
6057426
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