A Wind Power Prediction Method Based on Long Short-Term Memory and Adaptive Kriging-Subset Simulation Optimization
Accurate prediction of wind power is a prerequisite for implementing various regulatory and operational strategies and developing important technologies such as smart grids and advanced control systems.This paper presents a novel prediction method for wind power based on the long short-term memory and the adaptive Kriging-subset simulation optimization.Based on the extraction of data features from wind farms,with the goal of minimizing the root mean square error of long short-term memory predictions,and with hyperparameters as design variables,efficient hyperparameter optimization design is carried out through deep coupled adaptive Kriging-subset simulation optimization based on the expected improvement,and the output is the optimal predicted power.Finally,the prediction performance of the proposed method is verified through a case study.
wind power predictionlong short-term memoryadaptive Kriginghyper-parameter designsubset simulation optimization
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