Short-Term Probabilistic Forecasting Method for Wind Speed Combining Long Short-Term Memory and Gaussian Mixture Model

Abstract

Wind speed forecasting is advantageous in reducing wind-induced accidents or disasters and increasing the capture of wind power. Accordingly, this forecasting process has been a focus of research in the field of engineering. However, because wind speed is chaotic and random in nature, its forecasting inevitably includes errors. Consequently, specifying the appropriate method to obtain accurate forecasting results is difficult. The probabilistic forecasting method has considerable relevance to short-term wind speed forecasting because it provides both the predicted value and the error distribution. This study proposes a probabilistic forecasting method for short-term wind speeds based on the Gaussian mixture model and long short-term memory. The precision of the proposed method is evaluated by prediction intervals (i.e., prediction interval coverage probability, prediction interval normalized average width, and coverage width-based criterion) using 29 monitored wind speed datasets. The effects of wind speed characteristics on the forecasting precision of the proposed method were further studied. Results show that the proposed method is effective in obtaining the probability distribution of predicted wind speeds, and the forecast results are highly accurate. The forecasting precision of the proposed method is mainly influenced by the wind speed difference and standard deviation.