A wind speed interval forecasting system based on constrained lower upper bound estimation and parallel feature selection
作者：
Highlights：
• The LUBE approach is developed into a systematic framework for enhancement.
• A novel parallel feature selection module is proposed to extract vital factors.
• The original GOA is modified to address the constrained optimization problem.
• A so-called amnesia operator is proposed to improve the LUBE training process.
• The proposed system enhances the quality of wind speed interval forecasting.
摘要
•The LUBE approach is developed into a systematic framework for enhancement.•A novel parallel feature selection module is proposed to extract vital factors.•The original GOA is modified to address the constrained optimization problem.•A so-called amnesia operator is proposed to improve the LUBE training process.•The proposed system enhances the quality of wind speed interval forecasting.
论文关键词：LUBE,Lower upper bound estimation,SSA,Singular spectrum analysis,CEEMDAN,Complete ensemble empirical mode decomposition with adaptive noise,EMD,Empirical mode decomposition,IMF,Intrinsic mode function,FI,Forecasting interval,PICP,Prediction interval coverage probability,PINAW,Prediction interval normalized average width,CWC,Coverage width criterion,GOA,Grasshopper optimization algorithm,PSO,Particle swarm optimization,PFS,Parallel feature selection,WSRT,Wilcoxon signed-rank test,Wind speed interval forecasting,Feature selection,Grasshopper optimization algorithm,Decomposition,Metaheuristics
论文评审过程：Received 8 January 2021, Revised 21 August 2021, Accepted 22 August 2021, Available online 24 August 2021, Version of Record 4 September 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107435
