Ye Rui, Li Xutao, Ye Yunming, Zhang Baoquan
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China.
Neural Netw. 2022 Aug;152:118-139. doi: 10.1016/j.neunet.2022.04.004. Epub 2022 Apr 12.
Wind power is a new type of green energy. Though it is economical to access and gather such energy, effectively matching the energy with consumers' demand is difficult, because of the fluctuate, intermittent and chaotic nature of wind speed. Hence, multi-step wind speed prediction becomes an important research topic. In this paper, we propose a novel deep learning method, DyanmicNet, for the problem. DynamicNet follows an encoder-decoder framework. To capture the fluctuate, intermittent and chaotic nature of wind speed, it leverages a time-variant structure to build the decoder, which is different from conventional encoder-decoder methods. In addition, a new neural block (ST-GRU-ODE) is developed, which can model the wind speed in a continuous manner by using the neural ordinary differential equation (ODE). To enhance the prediction performance, a multi-step training procedure is also put forward. Comprehensive experiments have been conducted on two real-world datasets, where wind speed is recorded in the form of two orthogonal components namely U-Wind and V-Wind. Each component can be illustrated as wind speed images. Experimental results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art techniques.
风能是一种新型绿色能源。尽管获取和收集这种能源较为经济，但由于风速具有波动、间歇性和混沌的特性，要使能源与消费者需求有效匹配却很困难。因此，多步风速预测成为一个重要的研究课题。在本文中，我们针对该问题提出了一种新颖的深度学习方法——动态网络（DyanmicNet）。动态网络遵循编码器 - 解码器框架。为了捕捉风速的波动、间歇性和混沌特性，它利用时变结构构建解码器，这与传统的编码器 - 解码器方法不同。此外，还开发了一种新的神经块（ST - GRU - ODE），它可以通过使用神经常微分方程（ODE）以连续方式对风速进行建模。为了提高预测性能，还提出了一种多步训练过程。我们在两个真实世界数据集上进行了全面实验，其中风速以两个正交分量（即U风分量和V风分量）的形式记录。每个分量都可以表示为风速图像。实验结果证明了所提方法相对于现有技术的有效性和优越性。
