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contributor authorLi, He
contributor authorCheng, Long
contributor authorYang, Zhikai
contributor authorLiu, Pan
contributor authorMing, Bo
contributor authorWu, Chengjun
date accessioned2026-08-20T11:19:48Z
date available2026-08-20T11:19:48Z
date copyright2026/04/11
date issued2026
identifier otherJLEED9.EYENG-6457.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312038
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 ...
publisherAmerican Society of Civil Engineers
titleA Hybrid Decomposition–Deep Learning Framework for Accurate Forecasting in Hydro–Wind–Solar Systems
typeJournal Article
journal volume152
journal issue3
journal titleJournal of Energy Engineering
identifier doi10.1061/JLEED9.EYENG-6457
journal fristpage04026017-1
journal lastpage04026017-18
page18
treeJournal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 003
contenttypeFulltext


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