Abstract
Accurate wind speed forecasting is a fundamental requirement for advanced and economically viable large-scale wind power integration. The hybridization of the quaternion-valued neural networks and stationary wavelet transform has not been proposed before. In this paper, we propose a novel wind-speed forecasting model that combines the stationary wavelet transform with quaternion-valued neural networks. The proposed model represents wavelet subbands in quaternion vectors, which avoid separating the naturally correlated subbands. The model consists of three main steps. First, the wind speed signal is decomposed using the stationary wavelet transform into sublevels. Second, a quaternion-valued neural network is used to forecast wind speed components in the stationary wavelet domain. Finally, the inverse stationary wavelet transform is applied to estimate the predicted wind speed. In addition, a softplus quaternion variant of the RMSProp learning algorithm is developed and used to improve the performance and convergence speed of the proposed model. The proposed model is tested on wind speed data collected from different sites in China and the United States, and the results demonstrate that it consistently outperforms similar models. In the meteorological terminal aviation routine (METAR) dataset experiment, the proposed wind speed forecasting model reduces the mean absolute error, and root mean squared error of predicted wind speed values by 26.5% and 33%, respectively, in comparison to several existing approaches.
Highlights
Renewable energy plays an increasingly imperative role in the global energy market [1]
We propose a novel wind-speed forecasting model that combines the stationary wavelet transform (SWT) and quaternion-valued neural networks (QVNN)
The resulting subsignals in the SWT have the same length as the source signal, this is a desired property for the proposed wind speed forecasting model because this equality in length in wavelet subbands allows the formation of a quaternion vector representing the four different wavelet components of the signals at each time step
Summary
Renewable energy plays an increasingly imperative role in the global energy market [1]. The major contributions of this paper are as follows: We propose a novel wind-speed forecasting model that combines the stationary wavelet transform (SWT) and quaternion-valued neural networks (QVNN). The resulting subsignals in the SWT have the same length as the source signal, this is a desired property for the proposed wind speed forecasting model because this equality in length in wavelet subbands allows the formation of a quaternion vector representing the four different wavelet components of the signals at each time step. We enhance the developed quaternion RMSProp where each input signal is represented by a quaternion vector algorithm with a softplus function to further improve the containing information from the four different wavelet performance and convergence speed of the proposed subbands (Fig. 1). To accelerate the convergence rate [35]. v (k) is the secondorder quaternion momentum calculated as a combination of previous and current squared stochastic gradients
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