气候变化领域集成服务门户(CCPortal): Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

CCPortal

DOI	10.3390/atmos15030271
	Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm
	Zhou, Hao; Luo, Qi; Yuan, Ling
发表日期	2024
EISSN	2073-4433
起始页码	15
结束页码	3
卷号	15 期号:3
英文摘要	Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy's future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (similar to 2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m center dot s(-1). The overall wind speed distribution range is significant, meeting the requirements for wind farm development.
英文关键词	climate change scenarios; CMIP6; Shared Socioeconomic Pathways (SSP); numerical simulation; CNN; bias adjustment
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001192035400001
来源期刊	ATMOSPHERE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/295900
作者单位	China Meteorological Administration
推荐引用方式 GB/T 7714	Zhou, Hao,Luo, Qi,Yuan, Ling. Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm[J],2024,15(3).
APA	Zhou, Hao,Luo, Qi,&Yuan, Ling.(2024).Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm.ATMOSPHERE,15(3).
MLA	Zhou, Hao,et al."Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm".ATMOSPHERE 15.3(2024).