Ye Rui, Zhang Baoquan, Li Xutao, Ye Yunming
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 Oct;167:533-550. doi: 10.1016/j.neunet.2023.08.042. Epub 2023 Aug 26.
In wind speed prediction technologies, deep learning-based methods have achieved promising advantages. However, most existing methods focus on learning implicit knowledge in a data-driven manner but neglect some explicit knowledge from the physical theory of meteorological dynamics, failing to make stable and long-term predictions. In this paper, we explore introducing explicit physical knowledge into neural networks and propose Physical Equations Predictive Network (PEPNet) for multi-step wind speed predictions. In PEPNet, a new neural block called the Augmented Neural Barotropic Equations (ANBE) block is designed as its key component, which aims to capture the wind dynamics by combining barotropic primitive equations and deep neural networks. Specifically, the ANBE block adopts a two-branch structure to model wind dynamics, where one branch is physic-based and the other is data-driven-based. The physic-based branch constructs temporal partial derivatives of meteorological elements (including u-component wind, v-component wind, and geopotential height) in a new Neural Barotropic Equations Unit (NBEU). The NBEU is developed based on the barotropic primitive equations mode in numerical weather prediction (NWP). Besides, considering that the barotropic primitive mode is a crude assumption of atmospheric motion, another data-driven-based branch is developed in the ANBE block, which aims at capturing meteorological dynamics beyond barotropic primitive equations. Finally, the PEPNet follows a time-variant structure to enhance the model's capability to capture wind dynamics over time. To evaluate the predictive performance of PEPNet, we have conducted several experiments on two real-world datasets. Experimental results show that the proposed method outperforms the state-of-the-art techniques and achieve optimal performance.
在风速预测技术中，基于深度学习的方法已展现出显著优势。然而，大多数现有方法侧重于以数据驱动的方式学习隐含知识，却忽略了气象动力学物理理论中的一些显式知识，从而无法做出稳定且长期的预测。在本文中，我们探索将显式物理知识引入神经网络，并提出用于多步风速预测的物理方程预测网络（PEPNet）。在PEPNet中，一个名为增强正压方程（ANBE）块的新神经模块被设计为其关键组件，旨在通过结合正压原始方程和深度神经网络来捕捉风动力学。具体而言，ANBE块采用双分支结构对风动力学进行建模，其中一个分支基于物理，另一个分支基于数据驱动。基于物理的分支在一个新的神经正压方程单元（NBEU）中构建气象要素（包括u分量风、v分量风和位势高度）的时间偏导数。NBEU是基于数值天气预报（NWP）中的正压原始方程模式开发的。此外，考虑到正压原始模式是对大气运动较为粗略的假设，ANBE块中还开发了另一个基于数据驱动的分支，旨在捕捉正压原始方程之外的气象动力学。最后，PEPNet采用时变结构来增强模型随时间捕捉风动力学的能力。为评估PEPNet的预测性能，我们在两个真实世界数据集上进行了多项实验。实验结果表明，所提方法优于现有技术并取得了最优性能。
