Abstract
Based on the time evolution similarity principle of the topological neighbor phase points in the Phase Space Reconstruction (PSR), a new modeling approach of Short-Term Load Forecasting (STLF) with integrated dynamics mechanism and based on the fusion of the dynamic optimal neighbor phase points (DONP) and Improved Chaotic Neural Networks (ICNN) model was presented in this paper. The ICNN model can characterize complicated dynamics behavior. It possesses the sensitivity to the initial load value and to the walking of the whole chaotic track. The input dimension of ICNN is decided using PSRT, and the training samples are formed by means of the stepping dynamic space track on the basis of the DONP. So it can improve associative memory and generalization ability of ICNN model. The testing results show that proposed model and algorithm can enhance effectively the precision of STLF and its stability.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, Z., Sun, Y., Zhang, S. (2006). A New Modeling Approach of STLF with Integrated Dynamics Mechanism and Based on the Fusion of Dynamic Optimal Neighbor Phase Points and ICNN. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_123
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DOI: https://doi.org/10.1007/11760023_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
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