Enhanced Neuro-Fuzzy Architecture for Electrical Load Forecasting

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-fuzz

ENHANCED NEURO-FUZZY ARCHITECTURE FOR ELECTRICAL LOAD FORECASTING

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.

Struktur neuro-fuzzy tipe mimo-ts menggunakan LMA-training untuk electrical load time series data forecasting

Struktur jaringan neuro-fuzzy merupakan salah satu sistem yang dapat digunakan untuk meramalkan data time series. Salah satu data time series yang dapat diramalkan dengan menggunakan struktur jaringan neuro-fuzzy adalah data beban listrik suatu perusahaan atau negara. Struktur jaringan neuro-fuzzy yang digunakan untuk meramalkan beban listrik Jawa Timur-Bali ini, dibuat menggunakan Gaussian Membership Function sebagai fungsi membership dari Takagi Sugeno fuzzy logic system (FLS). Output dari sistem fuzzy ini kemudian dimasukan dalam sebuah algoritma training yaitu Levenberg-Marquardt algorithm (LMA) training untuk memperbaiki parameter-parameter yang dimiliki sehingga sistem dapat menghasilkan Output dengan error yang kecil. Data beban listrik yang digunakan untuk training adalah data beban listrik Jawa Timur-Bali dari 1 September 2005 - 31 Desember 2006. Hasil yang diperoleh dari pengujian sistem adalah untuk short term forecasting data beban listrik Jatim-Bali bulan Januari 2007 - Maret 2007, didapatkan Mean Square Error (MSE) sebesar 0,0010 dan untuk hasil long term forecasting bulan juni 2007 - Agustus 2007 didapatkan MSE sebesar 0.0011