LAUSR

Despite having plethora of works, wind speed time-series forecasting capabilities are prone to errors due to its intermittent and non-stationary nature as well as the limited generalization capabilities of forecasting methods for non-Gaussian distributed data. In this paper, a hybrid method that consists of elastic variational mode decomposition (eVMD) and forecasting random convolution nodes (fRCN) is proposed to forecast the Gaussian heteroscedastic wind speed time-series. The proposed eVMD algorithm gauges the non-stationary characteristics (complexity) of the wind speed signal and thereafter decomposes the signal into its intrinsic components (ICs) accordingly. The fRCN method rely on local receptive fields (LRF) to extract features that contribute to the local variations and the global trend in each IC. These features are subsequently learned using extreme learning machines (ELM) theories. An ensemble unit is employed to learn appropriate weightages for each forecasted IC before yielding the final forecasting values. Suitability of the proposed hybrid method for wind speed forecasting is evaluated via an actual wind speed dataset and comparing against various existing hybrid methods.