Achieving Optimal Demand-Side Integrated Energy Management via Hierarchical End-to-End Learning with Multisource NWP Data

dc.contributor.author	Dou, Zhenlan
dc.contributor.author	Zhang, Chunyan
dc.contributor.author	Pu, Chuanqing
dc.contributor.author	Jia, Kunqi
dc.contributor.author	Li, Zhonghao
dc.contributor.author	Zhou, Zihan
dc.date.accessioned	2026-08-20T11:19:19Z
dc.date.available	2026-08-20T11:19:19Z
dc.date.copyright	2025/12/08
dc.date.issued	2026
dc.identifier.other	JLEED9.EYENG-6342.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4312024
dc.description.abstract	AbstractAccurate modeling of renewable energy and load demand uncertainties, along with achieving optimal energy scheduling, are two critical components in the daily energy management of integrated energy systems (IES). This paper constructs an end-to-end ...
dc.publisher	American Society of Civil Engineers
dc.title	Achieving Optimal Demand-Side Integrated Energy Management via Hierarchical End-to-End Learning with Multisource NWP Data
dc.type	Journal Article
dc.journal.volume	152
dc.journal.issue	1
dc.journal.title	Journal of Energy Engineering
dc.identifier.doi	10.1061/JLEED9.EYENG-6342
dc.journal.fristpage	04025106-1
dc.journal.lastpage	04025106-14
dc.page	14
dc.tree	Journal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 001
dc.contenttype	Fulltext