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Erschienen in:
2010 | OriginalPaper | Buchkapitel
An Efficient Adaptive Fuzzy Neural Network (EAFNN) Approach for Short Term Load Forecasting
verfasst von : Juan Du, Meng Joo Er, Leszek Rutkowski
Erschienen in: Artificial Intelligence and Soft Computing
Verlag: Springer Berlin Heidelberg
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In this paper, an Efficient Adaptive Fuzzy Neural Network (EAFNN) model is proposed for electric load forecasting. The proposed approach is based on an ellipsoidal basis function (EBF) neural network, which is functionally equivalent to the TSK model-based fuzzy system. EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology are used to remove the unneeded hidden units. It can not only reduce the complexity of the network but also accelerate the learning speed. The proposed EAFNN method is tested on the actual electrical load data from well-known EUNITE competition data. Results show the proposed approach provides the superior forecasting accuracy when applying in the real data.