Abstract
In the thesis, a short-term forecasting method is presented on the basis of RBF neural network and fuzzy reasoning. In view of the problem that some influences on the regular electrical load are indeterminate, RBF neural network is used to seek universal law of load changes. With the easier formalization of load information and great flexibility shown while forecasting rule changes, fuzzy reasoning is introduced to analyze the maximum & minimum load. Then load forecasting results can be obtained with the integrated method, which not only makes full use of the self-adaption of neural network, but also takes the advantages of fuzzy reasoning while dealing with indeterminate factors. Examples prove that this method can increase the forecasting precision efficiently.
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© 2009 Springer-Verlag Berlin Heidelberg
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Lu, Y., Huang, Y. (2009). A Short-Term Load Forecasting Method Based on RBF Neural Network and Fuzzy Reasoning. In: Cao, B., Li, TF., Zhang, CY. (eds) Fuzzy Information and Engineering Volume 2. Advances in Intelligent and Soft Computing, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03664-4_120
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DOI: https://doi.org/10.1007/978-3-642-03664-4_120
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03663-7
Online ISBN: 978-3-642-03664-4
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