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JCR. 2013 Release (Thomson Reuters)
Freq.MonthlyISSN0039-7660
Impact
Factor
0,6235-Y-IF0,539
Immediacy
Index
0.12Cited
Half-life
>10.0
 
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Prof. Dr. B. N. BUSZEWSKI
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Office: UL BITWY WARSZAWSKIEJ 1920 R NR 3, WARSZAWA, POLAND, PL-02 362
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Title Some New Construction of Seasonal Smooth Support Vector Regression Firefly Algorithm in Electrical Load Forecasting
Paper ID vp0Fg
Author(s)Toni Toharudin, Rezzy Eko Caraka, Rung-Ching Chen, Sakhinah Abu Bakar, Resa Septiani Pontoh
AbstractAnalysis of electricity consumption is very important because it is related to the resilience of a country. To overcome the fluctuations in the data, data mining techniques, one of which is SVR, could be performed. The most important thing when creating a model is choosing the most suitable parameters for the SVR. In this article, the development of Seasonal Smooth Support Vector Newton-Armijo and Firefly algorithms is discussed in detail. The result shows that the firefly algorithm, one of the metaheuristic techniques, provides accuracy of MAPE=0.0243% RMSE=87.417, respectively.
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