A wind speed interval forecasting system based on constrained lower upper bound estimation and parallel feature selection

Abstract

Wind speed interval forecasting is being considered increasingly because it can provide more comprehensive information to address uncertainties in wind power generation. It helps ensure power quality, optimize power dispatching, and bring more economic and social benefits. Currently, the lower upper bound estimation (LUBE) approach is believed to provide outstanding performance in interval forecasting. However, considering the significant noise and irregular characteristics of wind speed, most existing LUBE models can either ineffectively learn the wind speed variation patterns behind the data or behave in a very unstable manner, leading to unsatisfactory results. Thus, this paper proposes a novel LUBE-based wind speed interval forecasting system based on an innovative parallel feature selection module for extracting the underlying vital historical variation patterns and a unique constrained LUBE training algorithm characterized by an amnesia operator to further guarantee the efficiency and stability of the LUBE training. The system effectiveness was demonstrated by performing experiments using two real datasets. The results show that the proposed system performs better than the naive, bootstrap, error analysis, and other LUBE models. It at least enhances the coverage width criterion by 1.8% and 6.8% for the two datasets, respectively.