Short-term load forecasting using multiscale bilinear recurrent neural network

Authors:
Dong-Chul Park;Chung Nguyen Tran;Yunsik Lee
Affiliations:
Dept. of Information Engineering, Myong Ji University, Korea;Dept. of Information Engineering, Myong Ji University, Korea;SoC Research Center, Korea Electronics Tech. Inst., Seongnam, Korea
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

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Abstract

In this paper, a short-term load forecasting model using wavelet-based neural network architecture termed a Multiscale BiLinear Recurrent Neural Network (M-BLRNN) is proposed. The M-BLRNN is a combination of several BiLinear Recurrent Neural Network (BLRNN) models. Each BLRNN predicts a signal at a certain resolution level obtained by the wavelet transform. The experiments and results on the load data from the North-American Electric Utility (NAEU) show that the M-BLRNN outperforms both a traditional MultiLayer Perceptron Type Neural Network (MLPNN) and the BLRNN in terms of the Mean Absolute Percentage Error (MAPE).