A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies

Cecati, Carlo; Kolbusz, Janusz; Rózycki, Paweł; Siano, Pierluigi; Wilamowski, Bogdan M.

doi:10.1109/TIE.2015.2424399

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.

Titolo:	A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies
Autori:	Cecati, Carlo Kolbusz, Janusz Rózycki, Paweł SIANO, Pierluigi Wilamowski, Bogdan M. mostra contributor esterni nascondi contributor esterni
Data di pubblicazione:	2015
Rivista:	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
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.
Handle:	http://hdl.handle.net/11386/4656492
Appare nelle tipologie:	1.1.2 Articolo su rivista con ISSN

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