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

作者：Cecati Carlo^*; Kolbusz Janusz; Rozycki Pawel; Siano Pierluigi; Wilamowski Bogdan M

来源：IEEE Transactions on Industrial Electronics, 2015, 62(10): 6519-6529.

DOI：10.1109/TIE.2015.2424399

• Decay radial basis RBF) neural networks
• error correction (ErrCor)
• extreme learning machines (ELMs)
• improved second order (ISO)
• neural networks
• RBF
• short-term load forecasting (STLF)
• support vector regression (SVR)

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 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.

• 出版日期2015-10