Decentralized Wind Power Forecasting Method Based on Informer

Background: Improving the accuracy of decentralized wind power forecasting has become an important means to ensure the safe and stable operation of the power grid. Traditional prediction methods cannot meet the requirements of precise wind power prediction for long time series. Methods: A decentralized wind power prediction model based on Informer is established. Introducing the model's sparse self-attention mechanism, self-attention distillation mechanism and The generative decoder model realizes the long-term sequence power prediction of decentralized wind power. Results: The results of the proposed model are analyzed by comparing it with traditional neural network models such as LSTM and feed-forward neural networks. The root mean square error (RMSE) can be reduced by up to 6.3%. Conclusion: It is proved that the Informer model prediction has a lower prediction error and performs better in long-term sequence power pre-tasks.