Wind Speed Forecasting Using New Adaptive Regressive Smoothing Models
This work proposes two new adaptive regressive smoothing models for wind speed forecasting (WSF) Generally WSF largely depends on the numerical models used which contribute the most error in WSF. This paper first uses most commonly used moving average and regressive smoothing models for WSF. Second, two new adaptive models are proposed for WSF. These adaptive models reduce the error in WSF by using difference of wind speeds with respect to the back period and current period wind speed values. In this paper model's quality measures are calculated by using mean error (ME) mean absolute error (MAE) mean square error (MSE) mean absolute percentage error (MAPE) and root means square error (RMSE) statistics. Simulations are performed to compare different models. It proves that the proposed models can improve the wind speed forecasting by effectively identifying and adjusting the errors from wind speed.