Offshore Wind Power Prediction Under Typhoon Weather Based on AGCN-BiLSTM-AM Model

Extreme weather such as typhoon leads to dramatic fluctuations in offshore wind power, which brings challenges to the safe and stable operation of power systems. Thus, it is particularly important to improve the accuracy of offshore wind power prediction under typhoon weather. To address the challenge of insufficient mining of complicated and dynamic spatio-temporal correlations of offshore wind farms under extreme weather in existing studies, this study proposes an AGCN-BiLSTM-AM based offshore wind power prediction model. Firstly, the spatial correlation of offshore wind farms topology is constructed as graphs, and an adaptive graph convolution network (AGCN) is then utilized to quantify and mine the topological changes due to wind fluctuation and wake effect. Secondly, the BiLSTM module is used to extract bidirectional temporal features to capture the drastic fluctuations of wind speed under typhoon weather. Additionally, the feature attention mechanism (AM) is introduced to enhance the learning of key features. Finally, the proposed model is verified on real wind farm data, with a higher prediction accuracy obtained under typhoon weather.