Application of NARX based FFNN, SVR and ANN Fitting models for long term industrial load forecasting and their comparison

Accurate load forecasting is essential for energy planning and load management. This paper presents long term industrial load forecasting (LTLF) using Nonlinear Autoregressive Exogenous model (NARX) based Feed-Forward Neural Network (FFNN) method, Support Vector Regression (SVR) and Neural Network models. It is applied to data sets obtained from National Transmission and Dispatch Company (NTDC) of Pakistan, ranging from 1970 to 2010. Several influencing load factors are examined. Comparison of results obtained by all three techniques is presented which portray a high acceptable accuracy with 2.09% Mean absolute percentage error (MAPE) on monthly and yearly demand estimation for industrial sector.