Short-Term load forecasting using multiscale bilinear recurrent neural network with an adaptive learning algorithm

Authors:
Chung Nguyen Tran;Dong-Chul Park;Hwan-Soo Choi
Affiliations:
ICRL, Dept. of Information Engineering, Myong Ji University, Korea;ICRL, Dept. of Information Engineering, Myong Ji University, Korea;ICRL, Dept. of Information Engineering, Myong Ji University, Korea
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 4
Cited 0

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution learning paradigm and signal prediction

IEEE Transactions on Signal Processing
Wavelet-based combined signal filtering and prediction

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Complex-bilinear recurrent neural network for equalization of a digital satellite channel

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.