Attention Mechanism Based CNN-LSTM-XGBoost Electric Power Meteorological Hybrid Forecasting Model of Typhoon Rainstorm
Major disasters such as typhoon rainstorm disasters have the characteristics of nonlinear,large range and multi-peak.To make the power grid obtain early warning information in time,the paper proposes a attention mechanism based CNN-LSTM-XGBoost electric power meteorological hybrid forecasting model of the typhoon rainstorm.Firstly,the attention mechanism based convolutional neural network is used to extract the key disaster characteristics of the typhoon rainstorm.Then the long short-term memory network(LSTM)is utilized to train time series prediction model and mine the time series feature information.To solve the problem of overfitting,the extreme gradient boosting algorithm is applied to replace model's output layer.Finally,typhoon Talim in 2023 is taken as a case study to verify the effectiveness of the proposed method.The results show that the proposed model has better performance,and its prediction accuracy is improved by more than 40.84%.
typhoon disasterrainstorm forecastneural networkhybrid modelpower grid early warning
万方数据
维普
