| contributor author | Li, He | |
| contributor author | Cheng, Long | |
| contributor author | Yang, Zhikai | |
| contributor author | Liu, Pan | |
| contributor author | Ming, Bo | |
| contributor author | Wu, Chengjun | |
| date accessioned | 2026-08-20T11:19:48Z | |
| date available | 2026-08-20T11:19:48Z | |
| date copyright | 2026/04/11 | |
| date issued | 2026 | |
| identifier other | JLEED9.EYENG-6457.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4312038 | |
| description abstract | AbstractAccurate 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 ... | |
| publisher | American Society of Civil Engineers | |
| title | A Hybrid Decomposition–Deep Learning Framework for Accurate Forecasting in Hydro–Wind–Solar Systems | |
| type | Journal Article | |
| journal volume | 152 | |
| journal issue | 3 | |
| journal title | Journal of Energy Engineering | |
| identifier doi | 10.1061/JLEED9.EYENG-6457 | |
| journal fristpage | 04026017-1 | |
| journal lastpage | 04026017-18 | |
| page | 18 | |
| tree | Journal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 003 | |
| contenttype | Fulltext | |