A multi-step wind power group forecasting seq2seq architecture with spatial–temporal feature fusion and numerical weather prediction correction

Abstract

Accurate total wind power forecasting is essential to wind farms. Conventional methods predict each wind turbine separately, which fails to exploit the spatial correlation between turbines and causes error accumulation through summation. Additionally, numerical weather prediction (NWP) with limited spatial–temporal resolutions is difficult to assist in power forecasting effectively, resulting in decreased performance as the forecasting steps increase. To address these drawbacks, a novel “N encoder-1 decoder” multi-channel fusion group forecasting sequence-to-sequence (seq2seq) architecture with wind resource quality assisted spatial attention (WRQASA) and NWP correction (NWPC) is proposed in this paper. The encoders in the architecture extract the temporal features of each turbine, followed by WRQASA adaptively adjusting their impact on the decoder. NWPC constructs micro-scale meteorological data based on its large-scale counterpart. The decoder takes the output from WRQASA and NWPC, and predicts the total power of the turbine group. The model is evaluated on a real-world wind farm. Ablation studies revealed the effectiveness of the novel architecture, WRQASA and NWPC. In 12, 24, 36 and 72-h multi-step forecasting scenarios, the new method outperformed conventional methods, such as seq2seq, light gradient boosting machine and support vector regression, achieving a more effective prediction of total power.