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dc.contributor.authorLi, He
dc.contributor.authorCheng, Long
dc.contributor.authorYang, Zhikai
dc.contributor.authorLiu, Pan
dc.contributor.authorMing, Bo
dc.contributor.authorWu, Chengjun
dc.date.accessioned2026-08-20T11:19:48Z
dc.date.available2026-08-20T11:19:48Z
dc.date.copyright2026/04/11
dc.date.issued2026
dc.identifier.otherJLEED9.EYENG-6457.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312038
dc.description.abstractAbstractAccurate forecasting in hydro–wind–solar systems is vital for multienergy coordination. However, traditional methods struggle to capture critical temporal patterns when relying solely on raw input data. This study proposes a hybrid decomposition–...The Graphical Abstract consists of three parts. The left part illustrates data input, including power output and their forecast factors. The middle part outlines decomposition and forecasting, where Improved Variational Mode Decomposition (IVMD) is ...
dc.publisherAmerican Society of Civil Engineers
dc.titleA Hybrid Decomposition–Deep Learning Framework for Accurate Forecasting in Hydro–Wind–Solar Systems
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue3
dc.journal.titleJournal of Energy Engineering
dc.identifier.doi10.1061/JLEED9.EYENG-6457
dc.journal.fristpage04026017-1
dc.journal.lastpage04026017-18
dc.page18
dc.treeJournal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 003
dc.contenttypeFulltext


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