Title :
Application of NARX based FFNN, SVR and ANN Fitting models for long term industrial load forecasting and their comparison
Author :
Awan, Shahid M. ; Khan, Zubair A. ; Aslam, M. ; Mahmood, Waqar ; Ahsan, Affan
Author_Institution :
Al-Khawarizmi Inst. of Comput. Sci., Univ. of Eng. & Technol., Lahore, Pakistan
Abstract :
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.
Keywords :
feedforward neural nets; load forecasting; load management; power engineering computing; regression analysis; support vector machines; ANN fitting models; LTLF; MAPE; NARX based FFNN application; NTDC; National Transmission and Dispatch Company; Pakistan; SVR fitting models; energy planning; feedforward neural network method; load factors; load management; long term industrial load forecasting; mean absolute percentage error; nonlinear autoregressive exogenous model; support vector regression; Artificial neural networks; Biological system modeling; Load forecasting; Load modeling; Predictive models; Support vector machines; Neural nonlinear systems; modelling; nonlinear estimation;
Conference_Titel :
Industrial Electronics (ISIE), 2012 IEEE International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-0159-6
Electronic_ISBN :
2163-5137
DOI :
10.1109/ISIE.2012.6237191
