Short term forecasting of surface layer wind speed using a continuous cascade model

TL;DR

This paper introduces a statistical short-term wind speed forecasting method based on continuous cascades and multifractal noise, demonstrating improved accuracy over traditional models in real-world data.

Contribution

The paper presents a novel continuous cascade model for wind speed forecasting that captures multifractal properties, outperforming existing methods.

Findings

Systematic improvement over persistence models

Better accuracy than Artificial Neural Networks

Effective on data from Corsica and Netherlands

Abstract

This paper describes a statistical method for short-term forecasting of surface layer wind velocity amplitude relying on the notion of continuous cascades. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal component and a fluctuating part represented by a "multifractal noise" associated with a random cascade. Performances of our model are tested on hourly wind speed series gathered at various locations in Corsica (France) and Netherlands. The obtained results show a systematic improvement of the prediction as compared to reference models like persistence or Artificial Neural Networks.