New Momentum Adjustment Technique for Levenberg -Marquardt Neural Network Training Used in Short Term Load Forecasting
عنوان مقاله: New Momentum Adjustment Technique for Levenberg -Marquardt Neural Network Training Used in Short Term Load Forecasting
شناسه ملی مقاله: PSC21_201
منتشر شده در بیست و یکمین کنفرانس بین المللی برق در سال 1385
شناسه ملی مقاله: PSC21_201
منتشر شده در بیست و یکمین کنفرانس بین المللی برق در سال 1385
مشخصات نویسندگان مقاله:
Khosravi.Z - Department of Power System Operation Niroo Research Institute (NRI) Tehran, IRAN
Barghinia - Department of Power System Operation Niroo Research Institute (NRI) Tehran, IRAN
Ansarimehr - Department of Power System Operation Niroo Research Institute (NRI) Tehran, IRAN
خلاصه مقاله:
Khosravi.Z - Department of Power System Operation Niroo Research Institute (NRI) Tehran, IRAN
Barghinia - Department of Power System Operation Niroo Research Institute (NRI) Tehran, IRAN
Ansarimehr - Department of Power System Operation Niroo Research Institute (NRI) Tehran, IRAN
One of the important requirements for operational planning of electrical utilities and also transactions of electrical power markets is the prediction of hourly load up to several days, known as short term load forecasting (STLF). Nowadays, intelligent methods, specially, artificial neural network (ANN) is the dominant method when it comes to STLF. The Levenberg-Marquardt (LM) algorithm has been extensively used as training method for ANNs. In this work, a new momentum adjustment technique is implemented for training ANN of Iran national power system (INPS) STLF. The performance is compared with conventional LM algorithm with other momentum adjustment techniques. The new method of momentum adjustment for LM algorithm improves learning of ANN for STLF of INPS in the sense of error and time consumption.
کلمات کلیدی: Short Term Load Forecasting, Artificial Neural Networks (ANN), Levenberg-Marquardt (LM) Algorithm
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/19785/