Multi Scale Graph Wavenet for Wind Speed Forecasting
This work addresses the problem of accurate wind speed forecasting for renewable energy applications, representing an incremental improvement over existing methods.
The paper tackles wind speed forecasting by proposing a novel deep learning architecture, Multi Scale Graph Wavenet, which outperforms state-of-the-art baseline models by 4-5% across multiple forecast horizons.
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%.