Wind Classification using Unsupervised Learning

Abstract

During the preparation for the Olympic Sailing Competition, held in 2021 in Tokyo, Japan, the Dutch National Sailing Team encountered days with unpredicted wind behaviour. To gain more understanding in the wind patterns occurring, a deep learning based approach is taken. The goal of this research is to find out if unsupervised learning methods can contribute to wind pattern classification. It can then be investigated if the classification can increase understanding in specific wind patterns. The input data for the unsupervised learning model consists of 40 years of reanalysis wind speed data of an area including Japan. To classify the wind patterns, the dimensionality of the input data is reduced using different autoencoders. This reduced dimensional form is then clustered using K-means clustering. The results of the K-means algorithm are compared and the best autoencoder is chosen. The resulting clusters are analyzed for extreme wind patterns, such as typhoons. It is expected that these wind patterns will be clustered together. To check this, the cluster containing typhoon Jebi, the typhoon which caused the highest insurance cost ever in Japan, is analyzed. If this cluster contains typhoons, unsupervised learning is able to provide useful information regarding wind patterns. The best working autoencoder used in this research is the 3D CNN autoencoder. Using the 3D CNN autoencoder, some clusters with specific wind patterns are found. The cluster containing typhoon Jebi consists of 95.8% of typhoons, from which it can be concluded that unsupervised learning is a valid method for wind pattern classification.