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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
engineering
A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies
IEEE Transactions on Industrial Electronics, Volume 62, No. 10, Article 7089261, Year 2015
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Description
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. © 2015 IEEE.
Authors & Co-Authors
Cecati, Carlo
Italy, L'aquila
Università Degli Studi Dell'aquila
Italy, L'aquila
Digipower Ltd.
Siano, Pierluigi
Italy, Salerno
Università Degli Studi Di Salerno
Statistics
Citations: 211
Authors: 2
Affiliations: 5
Identifiers
Doi:
10.1109/TIE.2015.2424399
ISSN:
02780046