基于新型时空预测模型 STICA 的超短期海上风电出力预测

The traditional offshore wind power prediction methods are difficult to fully capture the complex spatiotemporal correlation of offshore wind farm，which results in limited prediction accuracy. Therefore，a spatiotemporal interactive causal attention neural network（STICA） with excellent spatiotemporal information mining ability is designed for ultra-short-term offshore wind power prediction. The causal attention module is used to construct dependency relationship between variables and adaptively learn the correlation between wind turbine power and relevant variables. The dynamic graph convolutional network is used to dynamically generate the spatial connection between wind farms. Combining the sequence after correlation learning and the results generated by dynamic graph convolutional network，the hierarchical interactive time series decom⁃ position module is used for establishing the temporal relationship of multi-dimensional time data，which realizes effective ultra-short-term offshore wind power prediction. The measured data from offshore wind turbines is used for analysis and verification，and the results show that the proposed method has superior ultra-short-term offshore wind power prediction ability，and it has improved the accuracy and efficiency compared with the conventional prediction models.