Multi Scale Graph Wavenet for Wind Speed Forecasting

TL;DR

This paper introduces a Multi Scale Graph Wavenet, a deep learning model that effectively captures spatial and temporal dependencies in wind speed data, significantly improving forecasting accuracy over existing methods.

Contribution

The paper presents a novel deep learning architecture combining graph convolutions with multi-scale temporal modeling for wind speed prediction.

Findings

Outperforms state-of-the-art models by 4-5% on wind speed forecasting.

Effectively captures spatial and temporal relationships in multivariate weather data.

Demonstrates robustness across different cities and forecast horizons.

Abstract

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%.