Enhanced Neuro-Fuzzy Architecture for Electrical Load Forecasting
| Main Authors: | Ferdinando, Hany, Pasila, Felix, Kuswanto , Henry |
|---|---|
| Format: | Article PeerReviewed application/pdf |
| Terbitan: |
Universitas Ahmad Dahlan
, 2010
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| Subjects: | |
| Online Access: |
https://repository.petra.ac.id/16489/1/Telkomnika_Pasila.pdf https://repository.petra.ac.id/16489/ |
Daftar Isi:
- 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. Keywords: forecasting, LMA, neuro-fuzzy