气候变化领域集成服务门户(CCPortal): Estimating hub-height wind speed based on a machine learning algorithm:implications for wind energy assessment

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DOI	10.5194/acp-23-3181-2023
	Estimating hub-height wind speed based on a machine learning algorithm:implications for wind energy assessment
	Liu, Boming; Ma, Xin; Guo, Jianping; Li, Hui; Jin, Shikuan; Ma, Yingying; Gong, Wei
发表日期	2023
ISSN	1680-7316
EISSN	1680-7324
起始页码	3181
结束页码	3193
卷号	23 期号:5 页码:13
英文摘要	Accurate estimation of wind speed at wind turbine hub height is of significance for wind energy assessment and exploitation. Nevertheless, the traditional power law method (PLM) generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speed. This inevitably leads to significant uncertainties in estimating the wind speed profile especially under unstable conditions. To minimize the uncertainties, we here use a machine learning algorithm known as random forest (RF) to estimate the wind speed at hub heights such as at 120 m (WS120), 160 m (WS160), and 200 m (WS200). These heights go beyond the traditional wind mast limit of 100-120 m. The radar wind profiler and surface synoptic observations at the Qingdao station from May 2018 to August 2020 are used as key inputs to develop the RF model. A deep analysis of the RF model construction has been performed to ensure its applicability. Afterwards, the RF model and the PLM model are used to retrieve WS120, WS160, and WS200. The comparison analyses from both RF and PLM models are performed against radiosonde wind measurements. At 120 m, the RF model shows a relatively higher correlation coefficient R of 0.93 and a smaller RMSE of 1.09 m s(-1), compared with the R of 0.89 and RMSE of 1.50 m s(-1) for the PLM. Notably, the metrics used to determine the performance of the model decline sharply with height for the PLM model, as opposed to the stable variation for the RF model. This suggests the RF model exhibits advantages over the traditional PLM model. This is because the RF model considers well the factors such as surface friction and heat transfer. The diurnal and seasonal variations in WS120, WS160, and WS(200 )from RF are then analyzed. The hourly WS120 is large during daytime from 09:00 to 16:00 local solar time (LST) and reach a peak at 14:00 LST. The seasonal WS120 is large in spring and winter and is low in summer and autumn. The diurnal and seasonal variations in WS160 and WS200 are similar to those of WS120. Finally, we investigated the absolute percentage error (APE) of wind power density between the RF and PLM models at different heights. In the vertical direction, the APE is gradually increased as the height increases. Overall, the PLM algorithm has some limitations in estimating wind speed at hub height. The RF model, which combines more observations or auxiliary data, is more suitable for the hub-height wind speed estimation. These findings obtained here have great implications for development and utilization in the wind energy industry in the future.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000946530200001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273349
作者单位	Wuhan University; Chinese Academy of Meteorological Sciences (CAMS); Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Liu, Boming,Ma, Xin,Guo, Jianping,et al. Estimating hub-height wind speed based on a machine learning algorithm:implications for wind energy assessment[J],2023,23(5):13.
APA	Liu, Boming.,Ma, Xin.,Guo, Jianping.,Li, Hui.,Jin, Shikuan.,...&Gong, Wei.(2023).Estimating hub-height wind speed based on a machine learning algorithm:implications for wind energy assessment.ATMOSPHERIC CHEMISTRY AND PHYSICS,23(5),13.
MLA	Liu, Boming,et al."Estimating hub-height wind speed based on a machine learning algorithm:implications for wind energy assessment".ATMOSPHERIC CHEMISTRY AND PHYSICS 23.5(2023):13.