Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)

11 Short-term time series wind power predictions are extremely essential for accurate and efficient offshore 12 wind energy evaluation and, in turn, benefit large wind farm operation and maintenance (O&M). However, 13 it is still a challenging task due to the intermittent nature of offshore wind, which significantly increases 14 difficulties in wind power forecasting. In this paper, a novel hybrid model, using unique strengths of 15 Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), 16 and Deep-learning-based Long Short-Term Memory (LSTM), was proposed to handle different 17 components in the power time series of an offshore wind turbine in Scotland, where neither the 18 approximation nor the detail was considered as purely nonlinear or linear. Besides, an integrated pre-19 processing method, incorporating Isolation Forest (IF), resampling, and interpolation was applied for the 20 raw Supervisory Control and Data Acquisition (SCADA) datasets. The proposed DWT-SARIMA-LSTM 21 model provided the highest accuracy among all the observed tests, indicating it could efficiently capture 22 complex times series patterns from offshore wind power.