Min Joon Young, Cho Hyung Ki

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 4, No. 7, pp. 1749-1758, Jul. 1997
10.3745/KIPSTE.1997.4.7.1749,   PDF Download:

Abstract

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal''s GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps : SOFM and Learning Vector Quantization : LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal''s GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. 4th, Jun. 4th, Jul. 4th, Sep. 4th, Nov. 4th, 1995, after having trained the data for the days from Mar. 1th to 3th, from Jun. 1th to 3th, from Jul. 1th to 3th, from Sep. 1th to 3th, and from Nov. 1th to 3th, 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.

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