Selecting Input Factors for Clusters of Gaussian Radial Basis Function Networks to Improve Market Clearing Price Prediction

@inproceedings{Guo2001SelectingIF,
  title={Selecting Input Factors for Clusters of Gaussian Radial Basis Function Networks to Improve Market Clearing Price Prediction},
  author={Jau-Jia Guo and Peter B. Luh},
  year={2001}
}
In a deregulated power market, bidding decisions rely on good market clearing price prediction. One of the common forecasting methods is Gaussian radial basis function (GRBF) networks that approximate input–output relationships by building localized Gaussian functions (clusters). Currently, a cluster uses all the input factors. Certain input factors, however, may not be significant and should be deleted because they mislead local learning and result in poor predictions. Existing pruning methods… CONTINUE READING
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