Short-term heavy overload forecasting of public transformers based on combined lstm-xgboost model

HIGHLIGHTS

• who: Hao Ma et al. from the State Grid Hebei Marketing Service Center, Shijiazhuang, China School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China have published the research work: Short-Term Heavy Overload Forecasting of Public Transformers Based on Combined LSTM-XGBoost Model, in the Journal: Energies 2023, 16, x FOR PEER REVIEW of /2023/
• what: Spurred on by the deficiencies of the abovementioned methods, the authors propose a shortterm heavy overload prediction method based on the combined Long Short-Term Memory (LSTM) -XGBoost model. Short-Term Heavy Overload Prediction Model . . .
  

   

  

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