太阳能学报

基于机器学习方法的风速偏差订正研究

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太阳能学报 | 2026,47(6): 459-465

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太阳能学报 2026 , 47 (6) : 459 -465

基于机器学习方法的风速偏差订正研究

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贺姗姗, 王捷儒, 申彦波, 高金兵

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RESEARCH ON WIND SPEED DEVIATION CORRECTION BASED ON MACHINE LEARNING METHODS

He Shanshan, Wang Jieru, Shen Yanbo, Gao Jinbing

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doi: 10.19912/j.0254-0096.tynxb.2025-0286

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通过风速偏差订正方法提高模式产品的预报准确性,为风电场风电功率预测提供可靠的数据支持。以山西陵川县风电场为例,利用循环神经网络（RNN）、非线性模型（NLinear）、Transformer这3种机器学习方法,分别建立CMA-WSP2.0模式产品的风速偏差订正模型,研究结果表明,3种方法的结果均优于模式产品,且Transformer、NLinear分别在10 m和100 m高度风速提高准确性方面表现更优。因此,机器学习方法可有效提升风速数据的质量和可靠性,为风能资源开发利用、功率预报等领域提供更准确的数据支持。

关键词

机器学习 / 风速 / 偏差校正 / 数值天气预报 / 风电场 / 深度学习

Abstract

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By improving the forecasting accuracy of model products through wind speed deviation correction methods, reliable data support is provided for wind power generation forecasting in wind farms. Taking the Lingchuan County Wind Farm in Shanxi Province as an example, three machine learning methods recurrent neural network （RNN）, nonlinear model（NLinear）, and Transformer were employed to establish wind speed deviation correction models for CMA-WSP2.0 model products. The results show that the results of three methods are better than that of the original model products, and Transformer and NLinear perform better in improving the accuracy of wind speed at heights of 10 m meters and 100 m meters, respectively. Therefore, machine learning methods can effectively improve the quality and reliability of wind speed data, offering a more accurate data foundation for wind energy resource development and utilization, power forecasting and other fields.

Key words

machine learning / wind speed / bias correction / numerical weather prediction / wind farm / deep learning

引用本文

贺姗姗, 王捷儒, 申彦波, 高金兵. 基于机器学习方法的风速偏差订正研究. 太阳能学报, 2026 , 47 (6) : 459 -465 . DOI: 10.19912/j.0254-0096.tynxb.2025-0286

He Shanshan, Wang Jieru, Shen Yanbo, Gao Jinbing. RESEARCH ON WIND SPEED DEVIATION CORRECTION BASED ON MACHINE LEARNING METHODS[J]. Acta Energiae Solaris Sinica, 2026 , 47 (6) : 459 -465 . DOI: 10.19912/j.0254-0096.tynxb.2025-0286

基金

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内蒙古自治区揭榜挂帅项目（2024JBGS0054）

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2026年第47卷第6期

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doi: 10.19912/j.0254-0096.tynxb.2025-0286

接收时间：2025-02-20
首发时间：2026-07-17

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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