{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xiao F"],"funding":["State Grid Fujian Electric Power Company"],"pagination":["e0299955"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10959340"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["19(3)"],"pubmed_abstract":["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 validate"],"journal":["PloS one"],"pubmed_title":["Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting."],"pmcid":["PMC10959340"],"funding_grant_id":["SGTYHT/20-JS-223(SGFJJY00GHJS2200054)"],"pubmed_authors":["Ni J","Li X","Xiao F","Yang S"],"additional_accession":[]},"is_claimable":false,"name":"Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting.","description":"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 validate","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2025-04-22T13:04:55.603Z","creation":"2025-04-06T00:29:28.957Z"},"accession":"S-EPMC10959340","cross_references":{"pubmed":["38517881"],"doi":["10.1371/journal.pone.0299955"]}}