Short-term offshore wind speed forecast by seasonal ARIMA-A comparison against GRU and LSTM

Offshore wind power is one of the fastest-growing energy sources worldwide, which is environmentally friendly and economically competitive. Short-term time series wind speed forecasts are extremely sig-nificant for proper and efficient offshore wind energy evaluation and in turn, benefit wind farm owner, grid operators as well as end customers. In this study, a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model is proposed to predict hourly-measured wind speeds in the coastal/offshore area of Scotland. The used datasets consist of three wind speed time series collected at different ele-vations from a coastal met mast, which was designed to serve for a demonstration offshore wind turbine. To verify SARIMA's performance, the developed predictive model was further compared with the newly developed deep-learning-based algorithms of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). Regardless of the recent development of computational power has triggered more advanced machine learning algorithms, the proposed SARIMA model has shown its outperformance in the accu-racy of forecasting future lags of offshore wind speeds along with time series. The SARIMA model pro-vided the highest accuracy and robust healthiness among all the three tested predictive models based on corresponding datasets and assessed forecasting horizons. (c) 2021 Elsevier Ltd. All rights reserved.