Analysis on the Applicability of STMAS-WRF Three-Dimensional Wind Speed Forecast Product in Coastal Wind Farms

WU Ying; DING Yihan; LI Huanlian; HUANG Yilu; GU Tingting; WANG Wei

doi:10.16516/j.ceec.2025-345

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Analysis on the Applicability of STMAS-WRF Three-Dimensional Wind Speed Forecast Product in Coastal Wind Farms

更新时间：2026-10-14

- Analysis on the Applicability of STMAS-WRF Three-Dimensional Wind Speed Forecast Product in Coastal Wind Farms
- Southern Energy Construction Vol. 13, Issue 3, Pages: 129-137(2026)
- 作者机构：
  
  1. 浙江省气象服务中心,浙江,杭州,310057
  2. 中国气象局气象干部培训学院,北京,100081
- 作者简介：
- 基金信息：
- DOI：10.16516/j.ceec.2025-345
  CLC： TK89;TM715
- Published：2026
- 稿件说明：
移动端阅览
吴影, 丁艺涵, 李焕连, 等. STMAS-WRF三维风速预报产品在沿海风电场的适用性分析[J]. 南方能源建设, 2026, 13(3): 129-137.

.
吴影, 丁艺涵, 李焕连, 等. STMAS-WRF三维风速预报产品在沿海风电场的适用性分析[J]. 南方能源建设, 2026, 13(3): 129-137. DOI： 10.16516/j.ceec.2025-345.

. DOI： 10.16516/j.ceec.2025-345.

摘要

目的

随着“十四五”能源规划持续推进，我国风电产业已进入大规模、高比例发展的新阶段。对于风电场运营效率而言，提高风机风速预报的准确率至关重要。

方法

文章以沿海某风电场为代表，对STMAS-WRF模式三维风速预报产品的预报效果开展多尺度、全方位的检验评估。

结果

研究表明：（1）风机高度预报风速和实测风速趋势总体一致，预报风速整体较实测风速偏大。预报和实测风速存在较好的相关性，但随着预报时效的增加，准确率逐渐降低；（2）风机日、月平均风速预报趋势与实测风速一致，月平均风速预报偏差8月最小，6月次之；10月和11月预报偏差较大，说明秋季风速预报难度较大；（3）变化风速段和小风速段预报效果较额定风速段好，说明模式对于极值的预报能力存在不足；（4）A、B区风机预报效果较C、D区好，可能与C、D区地势更高有关，预报难度和复杂度增大。

结论

STMAS-WRF模式预报效果较好，且误差分布存在一定的规律性。下一步可对该产品进行系统性订正，提高风速预报准确率，服务于“双碳”目标下的风力资源评估及风电场风功率预报。

Abstract

Objective

With the continuous advancement of the "14th Five-Year Plan" for energy

China's wind power industry has entered a new stage of large-scale and high-proportion development. For the operational efficiency of wind farms

improving the accuracy of wind speed forecasts for wind turbines is of vital importance.

Method

This paper took the coastal wind farm as a representative to conduct an applicability study on the application ef

fect of the three-dimensional wind speed forecast product of the STMAS-WRF forecast model.

Result

The research shows that: (1) The trends of the predicted wind speed and the measured wind speed at the height of the wind turbine are generally consistent

and the predicted wind speed is generally larger than the measured wind speed. There is a good correlation between the forecast and the measured wind speed

but as the forecast period increases

the accuracy gradually decreases. (2) The forecast trends of the daily and monthly average wind speeds of the wind turbines are consistent with the measured wind speeds. The deviation of the monthly average wind speed forecast is the smallest in August and the second smallest in June. The forecast deviations in October and November are relatively large

indicating that it is more difficult to predict wind speed in autumn. (3) The forecast effect of the variable wind speed range and the low wind speed range is better than that of the rated wind speed range

indicating that the model's prediction ability for extreme values is insufficient. (4) The prediction effect of wind turbines in area A and area B is better than that in area C and area D

which may be related to the higher terrain in area C and area D

increasing the difficulty and complexity of prediction.

Conclusion

The prediction performance of the STMAS-WRF model is relatively good

and the error distribution has certain regularity. The next step is to systematically revise this product to improve the accuracy of wind speed forecasting

serving the wind resource assessment and wind power forecasting of wind farms under the "dual carbon" goals.

关键词

Keywords

references

TAO J Y, QIN C, JIN Y Q, et al. A review of load models for new power systems [J]. Power system protection and control, 2025, 53(5): 168-187. DOI: 10.19783/j.cnki.pspc.240390.

WANG H C, SONG H Y, YU J. Research on optimization scheme for new power system terminal communication network [J]. Shandong electric power, 2024, 51(2): 56-66. DOI: 10.20097/j.cnki.issn1007-9904.2024.02.006.

TIAN S X, JIANG H Z, QIN S Y, et al. Offshore wind power forecasting method based on CKAN-IWTC-LSTM [J]. Smart power, 2025, 53(11): 91-98. DOI: 10.20204/j.sp.2025.11011.

YANG M, LI M L, WANG B, et al. Review on technologies and applications of numerical weather prediction for new power system [J]. Automation of electric power systems, 2025, 49(17): 1-20. DOI: 10.7500/AEPS20240227001.

QU B Y, FU L S. Research on wind power prediction based on NWP wind speed correction and VMD-DBO-DELM residual modeling [J]. Shandong electric power, 2025, 52(2): 32-45. DOI: 10.20097/j.cnki.issn1007-9904.2025.02.004.

HUANG L, GAN H Y, LIU X J, et al. Ultra-short-term photovoltaic power generation prediction based on transformer encoder [J]. Smart power, 2024, 52(5): 16-22, 59. DOI: 10.3969/j.issn.1673-7598.2024.05.004.

ZHANG Y F, LI Y L, YE L, et al. Short-term wind power prediction method considering wind turbine operation status clustering under low-temperature conditions. [J]. Power generation technology, 2025, 46(2): 326-335. DOI: 10.12096/j.2096-4528.pgt.23129.

FAN C L, SHANG H, CHUANG X H, et al. Short-term wind power prediction based on multi-scale dominant component analysis and hybrid data-driven model [J]. Shandong electric power, 2025, 52(11): 100-107. DOI: 10.20097/j.cnki.issn1007-9904.240300.

LIU Y M, LIU H, GU X P. Power system cascading failure blocking control considering system observability [J]. Power system protection and control, 2025, 53(7): 52-62. DOI: 10.19783/j.cnki.pspc.240504.

ZHANG Z W, LIN L L, GAO S, et al. A machine learning model for hub-height short-term wind speed prediction [J]. Nature communications, 2025, 16(1): 3195. DOI: 10.1038/s41467-025-58456-4.

ZENG Z L, WU H L, LIU Z H, et al. Enhancing short-term wind speed prediction capability of numerical weather prediction through machine learning methods [J]. Journal of geophysical research: atmospheres, 2024, 129(24): e2024JD041822. DOI: 10.1029/2024JD041822.

WANG M, MENG D, XU P H, et al. Validation and evaluation of the China meteorological administration wind energy and solar energy forecasting system (CMA-WSP) in short-term wind resource forecasting [J]. Southern energy construction, 2024, 11(1): 73-84. DOI: 10.16516/j.ceec.2024.1.08.

ZHANG C, WANG J K, WEI L X, et al. Assessment of multi-model sea surface wind speed forecasts in the nearshore areas of China [J]. Marine forecasts, 2025, 42(1): 81-88. DOI: 10.11737/j.issn.1003-0239.2025.01.010.

ZHU Y H, ZHU S P, XIA P, et al. Forecast calibration of 10 m wind speed over Xinjiang based on a U-net neural network [J]. Chinese journal of atmospheric sciences, 2024, 48(6): 2300-2315. DOI: 10.3878/j.issn.1006-9895.2406.23152.

LIU R X, XIE Y F, LIU J. A preliminary study on the 3DVAR assimilation of the AMSU-a data in space-time multiscale analysis system [J]. Journal of tropical meteorology, 2017, 23(3): 314-322. DOI: 10.16555/j.1006-8775.2017.03.008.

YU H, ZHANG J, ZHU G J, et al. Dynamic assessment and early warning method of power facility flood risk based on a 2D hydrodynamic model and data fusion [J]. Power system protection and control, 2025, 53(15): 71-82. DOI: 10.19783/j.cnki.pspc.241005.

ZHANG T. Multi-source data fusion and application research base on LAPS/STMAS [D]. Nanjing: Nanjing University of Information Science Technology, 2013.

LI C, TANG Q H, CHEN Y, et al. An overview of progresses in LAPS and prospective applications in real time data service [J]. Advances in meteorological science and technology, 2017, 7(2): 32-38. DOI: 10.3969/j.issn.2095-1973.2017.02.005.

PAN C, WANG C, SUN H, et al. STAGN ultra-short-term wind power forecasting based on hyperparameter optimization and error correction [J]. Power system protection and control, 2025, 53(8): 117-129. DOI: 10.19783/j.cnki.pspc.240939.

DEPPE A J, GALLUS W A, TAKLE E S. A WRF ensemble for improved wind speed forecasts at turbine height [J]. Weather and forecasting, 2013, 28(1): 212-228. DOI: 10.1175/WAF-D-11-00112.1.

XIE Y, KOCH S, MCGINLEY J, et al. A space–time multiscale analysis system: a sequential variational analysis approach [J]. Monthly weather review, 2011, 139(4): 1224-1240. DOI: 10.1175/2010MWR3338.1.

YANG M, ZHANG S T, WANG B. Short-term wind power forecasting method based on a causal regularized extreme learning machine [J]. Power system protection and control, 2024, 52(11): 127-136. DOI: 10.19783/j.cnki.pspc.231097.

WANG X, CHEN X Y. Power prediction method for offshore wind farms based on temporal convolutional network-transformer [J]. Power generation technology, 2025, 46(6): 1123-1132. DOI: 10.12096/j.2096-4528.pgt.24251.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

The Impact of Extreme Weather on the Entire Process of Wind Power Development and Response Strategies

Validation and Evaluation of the China Meteorological Administration Wind Energy and Solar Energy Forecasting System (CMA-WSP) in Short-Term Wind Resource Forecasting

Research on the Method for the Reconstruction and Expansion of the Wind Farm Based on the Scada Data

Research on the Temperature Suppression Technology for Artificial Cavern Walls Based on Inflated Circular Airflow

Control Strategy and Application of Distributed Grid-Forming Energy Storage Systems Based on Engineering Practice

Related Author

Dan MENG

Zhenghong CHEN

Yang XU

Peng ZENG

Ming WANG

Yang CUI

Peihua XU

Ming WANG

Related Institution

Hubei Provincial Meteorological Service Center

Hubei Provincial Meteorological Energy Technology Development Center

Guangxi Zhuang Autonomous Region Lightning Protection Center

Hubei Meteorological Service Center

Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration

AI问答

⁰