Discrete wavelet transforms based hybrid approach to forecast wind speed time series

Abstract

The wind resources have been estimated by using physical models, statistical models, and artificial intelligence models. Wind power calculation helps us measure the annual energy that will sustain the balance between electricity generation and electricity consumption. Wind speed plays a significant role in calculating wind power, due to which here we focus on wind speed prediction. In this paper, hybrid models for wind speed forecasting have been proposed. The hybrid models are formed by combining the time series decomposition technique, that is, discrete wavelet transform (DWT), with statistical models, that is, autoregressive integrated moving average (ARIMA) and generalized autoregressive score (GAS), respectively. These hybrid models are referred to as DWT-ARIMA and DWT-GAS. DWT decomposes the original series into sub-series. After that, statistical models are applied to each sub-series for prediction. In the end, aggregate the prediction results of each sub-series to get the final forecasted series. For experimentation purposes, statistical and hybrid models are applied to various datasets that are taken from the NREL repository. In our studies, the hybrid version demonstrates better results in terms of accuracy and complexity, which indicates superior performance in most cases compared to the existing statistical models.