پیش بینی بار الکتریکی قدرت حالت دولتی کوتاه مدت در ماهاراشترا با تاکید ویژه بر تغییرات فصلی با استفاده از یک فاصله زمانی جدید با توجه به شبکه عصبی مجدد بر اساس مدل شبکه عصبی تاخیر زمان

Experimental results indicate that the FTLRNN with time delay neural network (TDNN) clearly outperformed the gamma and laguarre based short-term memory structure in various performance metrics such as mean square error (MSE), normalized MSE, correlation coefficient (r) and mean absolute percentage error (MAPE) during evaluation process. Empirical results show that the proposed dynamic NN model consistently performs well on daily, weekly, and monthly average basis in terms of prediction accuracy. It is noticed from the literature review that an optimally configured FTLRNN with multi-channel tapped delay line memory structure is not currently available to solve short-term electrical power load prediction. The proposed method gives acceptable errors in all seasons, months and on daily basis. The average prediction error on three weeks is obtained as low as 1.67%.