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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Short-Term Load Forecasting Using Multiscale BiLinear Recurrent Neural Network
verfasst von : Dong-Chul Park, Chung Nguyen Tran, Yunsik Lee
Erschienen in: PRICAI 2006: Trends in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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).