A STL decomposition-based deep neural networks for offshore wind speed forecasting

Abstract

Accurate prediction of offshore wind speed is of great significance for optimizing operation strategies of offshore wind power. Here, a novel hybrid algorithm based on seasonal-trend decomposition with loess (STL) and auto-regressive integrated moving average (ARIMA)- long short-term memory neural network (LSTM) is proposed to eliminate seasonal factors in wind speed and fully exert the advantages of ARIMA processing linear series and LSTM processing nonlinear series. Moreover, wind speed are comprehensively preprocessed and statistically analyzed. Then, we handle information leakage problem. Finally, STL-ARIMA-LSTM model is applied to wind speed forecasting on 3 time-scales. The proposed model has the highest accuracy and resolution for the trend and periodicity of wind speed, and the lag problem of very shortterm wind speed prediction can be solved. This study also shows that when predicting offshore wind speed, we can handle the strong intermittence, volatility and outliers in wind speed by gradually adjusting time scale.