Title :
Electrical load forecasting using echo state network
Author :
Rabin, Md Jubayer Alam ; Hossain, M. Shamim ; Ahsan, Md Shamim ; Mollah, Md Abdus Salim ; Kabir, A. N. M. Enamul ; Shahjahan, Md
Author_Institution :
Dept. of Electr. & Electron. Eng., Khulna Univ. of Eng. & Technol., Khulna, Bangladesh
Abstract :
An algorithm for half hourly electrical load forecasting based on echo state neural networks (ESN) is proposed in this paper. Electrical load forecasting is one of the most challenging real life time series prediction problems. This demands a dynamic network. ESN is a new epitome for using recurrent neural networks (RNNs) with a simpler training method. Several versions of ESN are discussed. The load profile is treated as time series signal. The forecasting performance of ESN is analysed on the basis of its key parameters. ESN is compared with feed forward neural network (FNN) and Bagged Regression trees. Simulation results demonstrate that the proposed ESN algorithms can obtain more accurate forecasting results than the FNN and Bagged Regression trees.
Keywords :
feedforward neural nets; load forecasting; power engineering computing; recurrent neural nets; regression analysis; time series; trees (mathematics); ESN; FNN; RNN; bagged regression trees; dynamic network; echo state network; electrical load forecasting; feed forward neural network; load profile; recurrent neural networks; time 0.5 hour; time series; time series prediction; Bagged Regression trees; Echo State Network (ESN); Electrical load forecasting; Feed forward neural network (FNN); Recurrent Neural Network (RNN);
Conference_Titel :
Computer and Information Technology (ICCIT), 2012 15th International Conference on
Conference_Location :
Chittagong
Print_ISBN :
978-1-4673-4833-1
DOI :
10.1109/ICCITechn.2012.6509763
