DocumentCode
3145440
Title
Reduction in power system load data training sets size using fractal approximation theory
Author
Indjic, Drago
Author_Institution
Fac. of Electr. Eng., Beograd Univ., Yugoslavia
fYear
1991
fDate
8-11 Apr 1991
Firstpage
446
Abstract
Summary form only given. Fractal dimension is further used in reconstruction of forecasting data by iterated function system. Due to the generalisation property of artificial neural networks, the proposed method results in significant savings in computational time. The original fractal structure of data is preserved in forecasting
Keywords
fractals; load forecasting; neural nets; power engineering computing; power system planning; training; artificial neural networks; computational time; forecasting; fractal approximation theory; power system load; training data set reduction; Approximation methods; Artificial neural networks; Backpropagation algorithms; Fractals; Load forecasting; Power system analysis computing; Power system measurements; Power system modeling; Power system planning; Power systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Compression Conference, 1991. DCC '91.
Conference_Location
Snowbird, UT
Print_ISBN
0-8186-9202-2
Type
conf
DOI
10.1109/DCC.1991.213315
Filename
213315
Link To Document