Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting

• 主要な著者: Benaouda D., Murtagh F., Starck J.-L., Renaud O.
• その他の著者: 15844746300
• フォーマット: 論文
• 出版事項: 2023
• 主題: Autoregression; General regression neural network; Load forecast; Multi-layer perceptron; Recurrent neural network; Resolution; Scale; Time series; Wavelet transform; Multilayer neural networks; Recurrent neural networks; Time series analysis; Wavelet transforms; article; artificial neural network; Australia; decomposition; electricity; forecasting; information processing; model; power supply; prediction; priority journal; signal processing; Multiple resolution decomposition; Multiscale autoregressive method; Electric loads

We propose a wavelet multiscale decomposition-based autoregressive approach for the prediction of 1-h ahead load based on historical electricity load data. This approach is based on a multiple resolution decomposition of the signal using the non-decimated or redundant Haar � trous wavelet transform...