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ENHANCED NEURO-FUZZY ARCHITECTURE FOR ELECTRICAL LOAD FORECASTING.
- Source :
-
Telkomnika . 2010, Vol. 8 Issue 2, p87-96. 10p. 2 Diagrams, 6 Charts, 5 Graphs. - Publication Year :
- 2010
-
Abstract
- Previous researches about electrical load time series data forecasting showed that the result was not satisfying. This paper elaborates the enhanced neuro-fuzzy architecture for the same application. The system uses Gaussian membership function (GMF) for Takagi-Sugeno fuzzy logic system. The training algorithm is Levenberg-Marquardt algorithm to adjust the parameters in order to get better forecasting system than the previous researches. The electrical load was taken from East Java-Bali from September 2005 to August 2007. The architecture uses 4 inputs, 3 outputs with 5 GMFs. The system uses the following parameters: momentum=0.005, gamma=0.0005 and wildness factor=1.001. The MSE for short term forecasting for January to March 2007 is 0.0010, but the long term forecasting for June to August 2007 has MSE 0.0011. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16936930
- Volume :
- 8
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Telkomnika
- Publication Type :
- Academic Journal
- Accession number :
- 59627545
- Full Text :
- https://doi.org/10.12928/telkomnika.v8i2.609