论文标题
使用多维卷积神经网络的风速预测
Wind speed prediction using multidimensional convolutional neural networks
论文作者
论文摘要
对于许多经济，商业和管理部门来说，准确的风速预测至关重要。本文介绍了一个基于风速预测任务的卷积神经网络（CNN）的新模型。特别是，我们表明，与经典的基于CNN的模型相比，提出的模型能够通过从输入数据的多个维度（视图）中学习基本的复杂输入 - 输入关系来更好地表征风数据的时空演化。提出的模型利用了时空多元多维历史天气数据，用于学习用于风向预测的新表示。我们在两个现实生活中的天气数据集上进行实验。数据集是丹麦和荷兰城市的测量值。将所提出的模型与传统的2-维CNN模型，一个具有注意力层的2D-CNN模型以及配备高尺度和深度可分离卷积的2D-CNN模型。
Accurate wind speed forecasting is of great importance for many economic, business and management sectors. This paper introduces a new model based on convolutional neural networks (CNNs) for wind speed prediction tasks. In particular, we show that compared to classical CNN-based models, the proposed model is able to better characterise the spatio-temporal evolution of the wind data by learning the underlying complex input-output relationships from multiple dimensions (views) of the input data. The proposed model exploits the spatio-temporal multivariate multidimensional historical weather data for learning new representations used for wind forecasting. We conduct experiments on two real-life weather datasets. The datasets are measurements from cities in Denmark and in the Netherlands. The proposed model is compared with traditional 2- and 3-dimensional CNN models, a 2D-CNN model with an attention layer and a 2D-CNN model equipped with upscaling and depthwise separable convolutions.
