biostudies-literatureXiao FState Grid Fujian Electric Power Companye0299955https://www.ebi.ac.uk/biostudies/studies/S-EPMC10959340biostudies-literatureUnknown19(3)Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validatePloS oneBranch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting.PMC10959340SGTYHT/20-JS-223(SGFJJY00GHJS2200054)Ni JLi XXiao FYang SfalseBranch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting.Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate2024-01-01T00:00:00Z20242025-04-22T13:04:55.603Z2025-04-06T00:29:28.957ZS-EPMC109593403851788110.1371/journal.pone.0299955