Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction

Numerical Weather Prediction (NWP), which provides approximate weather information in the next few days, is an essential feature in wind power forecasting (WPF). However, the forecasted wind speed in NWP (NWPWS) shows temporal lag compared to the actual wind speed. And the time-varying nature of the temporal lag challenges the WPF methods on selecting the NWP temporal features within valid time windows. To this end, this paper first summarizes the characteristics of the temporal lag and finds the weak inertia property within a few hours. Consequently, a series-wise mechanism, temporal lag attention (TLA), is proposed to extract valid NWP information for the ultra-short-term WPF by considering the temporal lag’s interference. Three fundamental parts included in TLA, Block-Sparse Attention Range, Lag Recognition, and Feature Fusion, are conducted sequentially. Block-Sparse Attention Range firstly screens out the critical range from the wide distribution of the temporal lag to simplify the subsequent calculation; Lag Recognition real-timely compares the series-wise similarity between actual wind speed and NWPWS time series to determine the top-k most likely temporal lags with their probability; Feature Fusion finally generates the lag-fixed weighted NWPWS as the valid NWP information. TLA has good scalability and can be integrated into the matured WPF model as the feature processing module. Real-world cases verify the accuracy improvement by integrating TLA into a modified encoder–decoder model (MED). Compared to the common point-wise attention mechanism, TLA can mitigate the negative influence of NWPWS temporal lag on WPF more effectively. Meanwhile, the sparse attention range is also proven to benefit the higher WPF accuracy and lower training cost.