间歇性
风力发电
计算机科学
可再生能源
功率(物理)
调度(生产过程)
实时计算
工程类
气象学
电气工程
运营管理
物理
量子力学
湍流
作者
Zhou Wu,Shaoxiong Zeng,Ruiqi Jiang,Haoran Zhang,Zhile Yang
出处
期刊:Energy
[Elsevier]
日期:2023-05-01
卷期号:270: 126906-126906
被引量:10
标识
DOI:10.1016/j.energy.2023.126906
摘要
Wind power is one of the most promising renewable energy for its abundant resources, economically competitive, and environmentally friendly. Nevertheless, the wind power is challenging used in the power generation system due to its intermittency. Therefore, to improve the utilization ratio of wind power, the common method is adopting a prediction model for scheduling the generation industries. However, the information offered by single-step models hardly assists managers control their generators, and existing multi-step prediction models ignore the temporal dependence among predicted steps. Thus, a hybrid method based on a deep-chain echo state network (DCESN) and variational mode decomposition (VMD) is proposed to enhance the mapping capability for wind power multi-step prediction. The multiple reservoirs of deep-chain echo state network are concatenated as a chain structure, which could congregate the temporal relations among future steps shown in visualized graphs. Three comparative experiments demonstrate that the proposed hybrid method has promising performance on wind power multi-step prediction.
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