Title :
A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies
Author :
Cecati, Carlo ; Kolbusz, Janusz ; Rozycki, Pawel ; Siano, Pierluigi ; Wilamowski, Bogdan M.
Abstract :
Because of their excellent scheduling capabilities, artificial neural networks (ANNs) are becoming popular in short-term electric power system forecasting, which is essential for ensuring both efficient and reliable operations and full exploitation of electrical energy trading as well. For such a reason, this paper investigates the effectiveness of some of the newest designed algorithms in machine learning to train typical radial basis function (RBF) networks for 24-h electric load forecasting: support vector regression (SVR), extreme learning machines (ELMs), decay RBF neural networks (DRNNs), improves second order, and error correction, drawing some conclusions useful for practical implementations.
Keywords :
Biological neural networks; ISO; Load forecasting; Radial basis function networks; Support vector machines; Training; Decay RBF Neural Networks; Decay radial basis function (RBF) neural networks; Error Correction; Extreme Learning Machines; Improved Second Order; Neural Networks; RBF; Radial Basis Function; Short-Term Load Forecasting; Support Vector Regression; error correction (ErrCor); extreme learning machines (ELMs); improved second order (ISO); neural networks; short-term load forecasting (STLF); support vector regression (SVR);
Journal_Title :
Industrial Electronics, IEEE Transactions on
DOI :
10.1109/TIE.2015.2424399
