Abstract
A Radius Basic Function (RBF) neural network is proposed for the power load forecasting. RBF neural network can meet nonlinear recognition and process predition of the dynamic system, and has better adaptability to dynamic forecasting and prediction problem in mechnism. The RBF centres are determined by the orthogonal least squared (OLS) learning procedure. The effectiveness of the model and algorithm with the example of power load forecasting have been proved and approximation capability and learning speed of RBF neural network is better than BP neural network.
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Liang, Y. (2010). Application of RBF Neural Network in Short-Term Load Forecasting. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_1
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DOI: https://doi.org/10.1007/978-3-642-16530-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16529-0
Online ISBN: 978-3-642-16530-6
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