Abstract
In this paper, a short-term load forecasting model using a Multiscale BiLinear Recurrent Neural Network with an adaptive learning algorithm (M-BLRNN(AL)) is proposed. The proposed M-BLRNN(AL) model is based on a wavelet-based neural network architecture formulated by a combination of several individual BLRNN models. The wavelet transform adopted in the M-BLRNN(AL) is employed to decompose the load curve into a mutiresolution representation. Each individual BLRNN model is used to forecast the load signal at each resolution level obtained by the wavelet transform. The learning process is further improved by applying an adaptive learning algorithm at each resolution level. Experiments and results on load data from the North-American Electric Utility (NAEU) show that the proposed M-BLRNN(AL) model converges faster and archives better forecasting performance in comparison with other conventional models.
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Tran, C.N., Park, DC., Choi, HS. (2006). Short-Term Load Forecasting Using Multiscale BiLinear Recurrent Neural Network with an Adaptive Learning Algorithm. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_106
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DOI: https://doi.org/10.1007/11893257_106
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