A novel multi-layer stacking ensemble wind power prediction model under Tensorflow deep learning framework considering feature enhancement and data hierarchy processing

Abstract

Wind power prediction is crucial for energy production, but due to the complicated data characteristics of wind farms, it's difficult to accurately predict wind power output and it is challenging for a single prediction model to properly handle multi-featured time-series data sets. To solve the above problem, the density-based spatial clustering of applications with noise (DBSCAN) method was used to find outliers of data sets, and the recursive feature elimination (RFE) method was used to screen out main features according to importance. Sequentially a multi-layer stacking ensemble learning prediction model was constructed based on data hierarchy processing and feature enhancement techniques. Seven groups of ablation experiments were set to verify the model's effectiveness with the annual time-series data. The experimental results show that the DBSCAN method can effectively find outliers in wind data sets and improve the prediction accuracy and the RFE method can significantly reduce the computing time and improves the generalization ability and prediction accuracy. The multi-layer stacking ensemble model can slow down the overfitting trend of a single prediction model, effectively avoid the model from falling into the local search state, and obtain a strong learner with better generalization ability.