Short‐term wind power prediction based on stacked denoised auto‐encoder deep learning and multi‐level transfer learning

IF 5.4 3区材料科学 Q2 CHEMISTRY, PHYSICAL

ACS Applied Energy Materials Pub Date : 2023-07-13 DOI:10.1002/we.2856

Xiaosheng Peng, Zimin Yang, Yinhuan Li, Bo Wang, Jianfeng Che

引用次数: 0

查看原文本刊更多论文

基于叠加去噪自动编码器深度学习和多级迁移学习的短期风电预测

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

ACS Applied Energy Materials Materials Science-Materials Chemistry

CiteScore

10.30

自引率

6.20%

发文量

1368

期刊介绍： ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.