A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction

TL;DR

This paper introduces a hybrid wind speed prediction model combining wavelet decomposition, AR, and SVM techniques to enhance short-term forecasting accuracy in wind energy management.

Contribution

It presents a novel hybrid approach that decomposes wind speed data into frequency components and models them separately using AR and SVM, improving prediction accuracy.

Findings

Hybrid model outperforms standalone AR and SVM models.

Wavelet decomposition effectively captures different frequency components.

Significant accuracy improvement in short-term wind speed prediction.

Abstract

Wind speed modelling and prediction has been gaining importance because of its significant roles in various stages of wind energy management. In this paper, we propose a hybrid model, based on wavelet transform to improve the accuracy of the short-term forecast. The wind speed time series are split into various frequency components using wavelet decomposition technique, and each frequency components are modelled separately. Since the components associated with the high- frequency range shows stochastic nature, we modelled them with autoregressive (AR) method and rest of low-frequency components modelled with support vector machine (SVM). The results of the hybrid method show a promising improvement in accuracy of wind speed prediction compared to that of stand-alone AR or SVM model.