Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model

被引：0

作者：

Yi Fang ^{[1
,2
]}

Yunfei Wu ^{[1
]}

Fengmin Wu ^{[3
]}

Yan Yan ^{[4
]}

Qi Liu ^{[5
,2
]}

Nian Liu ^{[5
,2
]}

Jiangjiang Xia ^{[5
,2
]}

机构：

[1] Key Laboratory of Middle Atmosphere and Global Environment Observaaon,Institute of Atmospheric Physics,Chinese Academy of Sciences

[2] College of Earth and Planetary Sciences,University of Chinese Academy of Sciences

[3] Zhejiang Insatute of Meteorological Sciences

[4] 93110 Troops,People's Liberation Army of China

[5] Key Laboratory of Regional Climate-Environment for Temperate East Asia,Institute of Atmospheric Physics,Chinese Academy of Sciences

来源：

Atmospheric and Oceanic Science Letters | 2023年 / 16卷 / 04期

关键词：

机器学习; 极端梯度提升算法; 风速; 后处理; 平均杂质减少;

D O I：

暂无

中图分类号：

P457.5 [风预报];

学科分类号：

0706 ; 070601 ;

摘要：

准确的风速预报具有重要的社会意义.在本研究中,使用名为WSFBC-XGB的XGBoost机器学习模型对中国浙江省杭州市自动气象站的短期风速预报误差进行校正.WSFBC-XGB使用本地数值天气预报系统的产品作为输入.将WSFBC-XGB校正的结果与传统MOS(模型输出统计)方法校正的结果进行了比较.结果表明:WSFBCXGB预报风速的均方根误差(RMSE)/准确率(ACC)分别比NWP和MOS降低/提高了26.1%和7.64%/35.6%和7.02%;对于90%的站点WSFBC-XGB的RMSE/ACC均小于/高于MOS.此外,采用平均杂质减少法对WSFBC-XGB的可解释性进行分析,以帮助用户增加对模型的信任.结果表明:10米风速(47.35%),10米风的经向分量(12.73%),日循环(9.97%)和1000百帕风的经向分量(7.45%)是前4个最重要的特征.WSFBC-XGB模型将有助于提高短期风速预报的准确性,为大型户外活动提供支持.

引用

页码：39 / 46

页数：8

共 50 条

[1] Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model
Fang, Yi
Wu, Yunfei
Wu, Fengmin
Yan, Yan
Liu, Qi
Liu, Nian
Xia, Jiangjiang
[J]. ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2023, 16 (04)
[2] Short-term wind speed forecasting model based on relevance vector machine
ALSTOM Grid Technology Center Co., Ltd., Shanghai 201114, China
不详
不详
[J]. Li, H., 1600, Electric Power Automation Equipment Press (33):
[3] A Hybrid Short-Term Wind Speed Forecasting Model Based on Wavelet Decomposition and Extreme Learning Machine
Zhang, Yihui
Wang, He
Hu, Zhijian
Wang, Kai
Li, Yan
Huang, Dongshan
Ning, Wenhui
Zhang, Chengxue
[J]. ENERGY DEVELOPMENT, PTS 1-4, 2014, 860-863 : 361 - +
[4] Short-term Wind Speed Forecasting using Machine Learning Algorithms
Fonseca, Sebastiao B.
de Oliveira, Roberto Celio L.
Affonso, Carolina M.
[J]. 2021 IEEE MADRID POWERTECH, 2021,
[5] Short-term wind speed forecasting based on a hybrid model
Zhang, Wenyu
Wang, Jujie
Wang, Jianzhou
Zhao, Zengbao
Tian, Meng
[J]. APPLIED SOFT COMPUTING, 2013, 13 (07) : 3225 - 3233
[6] Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
Daniel, Lucky O.
Sigauke, Caston
Chibaya, Colin
Mbuvha, Rendani
[J]. ALGORITHMS, 2020, 13 (06)
[7] A hybrid intelligent framework for forecasting short-term hourly wind speed based on machine learning
Wang, Yelin
Yang, Ping
Zhao, Shunyu
Chevallier, Julien
Xiao, Qingtai
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2023, 213
[8] Short-term wind speed prediction using an extreme learning machine model with error correction
Wang, Lili
Li, Xin
Bai, Yulong
[J]. ENERGY CONVERSION AND MANAGEMENT, 2018, 162 : 239 - 250
[9] Short-term Wind Speed Forecasting with ARIMA Model
Radziukynas, Virginijus
Klementavicius, Arturas
[J]. 2014 55TH INTERNATIONAL SCIENTIFIC CONFERENCE ON POWER AND ELECTRICAL ENGINEERING OF RIGA TECHNICAL UNIVERSITY (RTUCON), 2014, : 145 - 149
[10] Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application
Niu, Mingfei
Sun, Shaolong
Wu, Jie
Zhang, Yuanlei
[J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2015, 2015

← 1 2 3 4 5 →