Abstract
Rough Set Theory (RST) is a powerful mathematics tool, which can deal with fuzzy and uncertain knowledge, and radial basis function (RBF) neural network has the ability to approach any nonlinear function precisely. According to the non-linear relation characteristics of ship power load, a short-term load prediction method based on RST and RBF neural network is presented in this paper. Using RST on the advantage of data analysis, the important input nodes can be selected, followed by a second stage selecting the important centers and leaning the weights of hidden nodes. The experimental results proved that this method could achieve greater predictive accuracy and generalization ability.
This work was supported by National Natural Science Foundation of China (60074004) and Science Foundation of Shanghai Education (03IK09, 04IK02).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Papalexopopoulos, A.D., Hesterberg, T.C.: A Regression Based Approach to Short-term Load Forecasting. IEEE Trans. Power Systems 5, 1535–1547 (1990)
Park, J.H., Lee, K.Y.: Composite Modeling for Adaptive Short-term Load Forecasting. IEEE Trans. Power Systems 6, 450–457 (1991)
Wang, X., Zhu, S.: Ship Power Load Forecasting Using Support Vector Machine. Proceedings of CSEE 24, 36–39 (2004)
Fuhaid, A.S., Sayed, M.A., Mahmoud, M.S.: Cascaded Artificial Neural Networks for Short-term Load Forecasting. IEEE Trans. Power Systems 12, 1524–1529 (1997)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Science 11, 341–356 (1982)
Xiao, J., Zhang, T.: New Rough Set Approach to Knowledge Reduction in Decision Table. In: Proceedings of the Third International Conference on Machine Learning and Cybernetics, pp. 2208–2211 (2004)
Xiao, J.-M., Wang, X.-H.: Highway Traffic Flow Model Using FCM-RBF Neural Network. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 956–961. Springer, Heidelberg (2004)
Wu, X., Song, Y., Wang, Y.: Estimation Model for Loads of Ship Power System based on Fuzzy SOFM Network. Ship Building of China 44, 65–70 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiao, J., Zhang, T., Wang, X. (2005). Ship Power Load Prediction Based on RST and RBF Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_103
Download citation
DOI: https://doi.org/10.1007/11427469_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)