ENHANCED NEURO-FUZZY ARCHITECTURE FOR ELECTRICAL LOAD FORECASTING
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.
Penulis: Hany Ferdinandoa,
Felix Pasila, Henry Kuswanto
Kode Jurnal: jptkomputerdd100031
