Deep Structure of Gaussian Kernel Function Networks for Predicting Daily Peak Power Demands

This paper proposes a novel method of predicting daily peak power demands using the deep structure of Gaussian kernel function networks (GKFNs). For the prediction model, the whole time series is divided into multiple parts and each part is trained using a GKFN. Then, the trained GKFNs are combined using the deep structure of GKFNs to minimize the mean square errors (MSEs) of prediction model. As a consequence, the proposed deep structure of GKFNs provides an improved performance of prediction accuracy compared with canonical GKFNs. The simulation for predicting daily peak power demands in Korea reveals that the proposed prediction model has the merits in prediction performances compared with the GKFN model and also other prediction models such as the k-NN and SVR.

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