School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China.
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China.
Neural Netw. 2023 May;162:147-161. doi: 10.1016/j.neunet.2023.02.024. Epub 2023 Feb 22.
Regional wind speed prediction plays an important role in the development of wind power, which is usually recorded in the form of two orthogonal components, namely U-wind and V-wind. The regional wind speed has the characteristics of diverse variations, which are reflected in three aspects: (1) The spatially diverse variations of regional wind speed indicate that wind speed has different dynamic patterns at different positions; (2) The distinct variations between U-wind and V-wind denote that U-wind and V-wind at the same position exhibit different dynamic patterns; (3) The non-stationary variations of wind speed represent that the intermittent and chaotic nature of wind speed. In this paper, we propose a novel framework named Wind Dynamics Modeling Network (WDMNet) to model the diverse variations of regional wind speed and make accurate multi-step predictions. To jointly capture the spatially diverse variations and the distinct variations between U-wind and V-wind, WDMNet leverages a new neural block called Involution Gated Recurrent Unit Partial Differential Equation (Inv-GRU-PDE) as its key component. The block adopts involution to model spatially diverse variations and separately constructs hidden driven PDEs of U-wind and V-wind. The construction of PDEs in this block is achieved by a new Involution PDE (InvPDE) layers. Besides, a deep data-driven model is also introduced in Inv-GRU-PDE block as the complement to the constructed hidden PDEs for sufficiently modeling regional wind dynamics. Finally, to effectively capture the non-stationary variations of wind speed, WDMNet follows a time-variant structure for multi-step predictions. Comprehensive experiments have been conducted on two real-world datasets. Experimental results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art techniques.
区域风速预测在风力发电的发展中起着重要作用，通常以两个正交分量的形式记录，即 U 向风速和 V 向风速。区域风速具有多变的特点，体现在三个方面：（1）区域风速的空间多变性表明风速在不同位置具有不同的动力模式；（2）U 向风速和 V 向风速的明显变化表明同一位置的 U 向风速和 V 向风速表现出不同的动力模式；（3）风速的非平稳变化表示风速的间歇性和混沌性。在本文中，我们提出了一种名为 Wind Dynamics Modeling Network（WDMNet）的新框架，用于模拟区域风速的多变性并进行准确的多步预测。为了共同捕捉空间多变性和 U 向风速与 V 向风速之间的明显变化，WDMNet 利用了一种名为 involution gated recurrent unit partial differential equation（Inv-GRU-PDE）的新神经模块作为其关键组件。该模块采用 involution 来模拟空间多变性，并分别构建 U 向风速和 V 向风速的隐藏驱动 PDE。该模块中的 PDE 构造是通过新的 involution PDE（InvPDE）层实现的。此外，在 Inv-GRU-PDE 模块中还引入了一个深度数据驱动模型作为对构建的隐藏 PDE 的补充，以充分模拟区域风动力学。最后，为了有效地捕捉风速的非平稳变化，WDMNet 采用了一种时变结构进行多步预测。在两个真实数据集上进行了综合实验。实验结果表明，该方法优于最新技术。
