Geometric deep learning has gained tremendous attention in both academia and industry due to its inherent capability of representing arbitrary structures. Due to exponential increase in interest towards renewable sources of energy, especially wind energy, accurate wind speed forecasting has become very important. . In this paper, we propose a novel deep learning architecture, Multi Scale Graph Wavenet for wind speed forecasting. It is based on a graph convolutional neural network and captures both spatial and temporal relationships in multivariate time series weather data for wind speed forecasting. We especially took inspiration from dilated convolutions, skip connections and the inception network to capture temporal relationships and graph convolutional networks for capturing spatial relationships in the data. We conducted experiments on real wind speed data measured at different cities in Denmark and compared our results with the state-of-the-art baseline models. Our novel architecture outperformed the state-of-the-art methods on wind speed forecasting for multiple forecast horizons by 4-5%.

翻译：由于对可再生能源的兴趣,特别是风能,准确的风速预测变得非常重要。在本论文中,我们提出了一个新的深层次学习结构,即用于风速预报的多比例图波网。它基于一个图形进化神经网络,在多变时间序列天气数据中捕捉空间和时间关系,以进行风速预报。我们特别从变速变速、跳过连接和初始网络中汲取灵感,以捕捉时间关系和图形变速网络,以捕捉数据中的空间关系。我们进行了在丹麦不同城市测量的实际风速数据实验,并将我们的结果与最先进的基线模型进行比较。我们的新结构将多种预测地平线的风速预报比最新方法高出4-5%。