Abstract
The accurate wind speed series forecast is very pivotal to security of grid dispatching and the application of wind power. Nevertheless, on account of their nonlinear and non-stationary nature, their short-term forecast is extremely challenging. Therefore, this dissertation raises one short-term wind speed forecast pattern on the foundation of attention with an improved gated recurrent neural network (AtGRU) and a tactic of error correction. That model uses the AtGRU model as the preliminary predictor and the GRU model as the error corrector. At the beginning, SSA (singular spectrum analysis) is employed in previous wind speed series for lessening the noise. Subsequently, historical wind speed series is going to be used for the predictor training. During this process, the prediction can have certain errors. The sequence of these errors processed by variational modal decomposition (VMD) is used to train the corrector of error. The eventual forecast consequence is just the sum of predictor forecast and error corrector. The proposed SSA-AtGRU-VMD-GRU model outperforms the compared models in three case studies on Woodburn, St. Thomas, and Santa Cruz. It is indicated that the model evidently enhances the correction of the wind speed forecast.
Abstract (translated)
准确的风速系列预测对电网调度安全和风能应用具有至关重要的意义。然而，由于其非线性和非平稳性质，其短期预测极其具有挑战性。因此，本文在关注的基础上提出了一种基于改进的门控循环神经网络（AtGRU）的短期风速预测模式，并采用错误纠正策略。该模型使用AtGRU模型作为初步预测器，GRU模型作为错误纠正器。在开始时，采用斯奇谱分析（SSA）来降低噪声。随后，历史风速系列将用于预测训练。在这个过程中，预测可能会出现一定误差。这些误差通过变分模态分解（VMD）处理后的序列用于错误纠正。最终预测结果仅是预测预报和误差纠正之和。与比较模型相比，所提出的SSA-AtGRU-VMD-GRU模型在伍德伯里、圣托马斯和圣克鲁斯三个案例研究中都表现出色。研究结果表明，该模型明显增强了风速预测的纠正。
URL
https://arxiv.org/abs/2404.11422
