引用本文:朱天宇,叶强,郝建树,高超越,杨家祺.基于LSTM的风矢量预测方法[J].电力自动化设备,2023,43(11):111-116
ZHU Tianyu,YE Qiang,HAO Jianshu,GAO Chaoyue,YANG Jiaqi.Wind vector prediction method based on LSTM[J].Electric Power Automation Equipment,2023,43(11):111-116
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基于LSTM的风矢量预测方法
朱天宇1, 叶强1, 郝建树1, 高超越1, 杨家祺2
1.哈尔滨工业大学 经济与管理学院,黑龙江 哈尔滨 150006;2.东北林业大学 机电工程学院,黑龙江 哈尔滨 150040
摘要:
从特征工程角度对风速与风向间的相关性进行分析,结果表明,风速与风向包含的特征信息不同,可以同时将其作为输入变量,用于训练模型。该结果也为输入变量时间长度的选择提供了依据。将风速分解为东西及南北方向2个正交的一维变量,以防止多维变量增加方法复杂度。采用长短时记忆神经网络(LSTM)分别对2个方向风速训练预测模型,并将预测结果还原为风速与风向预测数据。实验结果表明,所提方法能够更好地捕捉风速与风向中的信息量,在风速与风向的预测误差分别小于1.0 m/s和5°时,预测准确率可达到90%以上。
关键词:  风矢量预测方法  长短时记忆神经网络  特征工程  相关性  方法复杂度  信息密度
DOI:10.16081/j.epae.202305013
分类号:TM614
基金项目:国家自然科学基金创新群体项目基金资助项目(72121001)
Wind vector prediction method based on LSTM
ZHU Tianyu1, YE Qiang1, HAO Jianshu1, GAO Chaoyue1, YANG Jiaqi2
1.School of Management, Harbin Institute of Technology, Harbin 150006, China;2.College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Abstract:
The correlation between wind speed and wind direction is analyzed from the perspective of feature engineering, and the results show that wind speed and wind direction contain different feature information, which can be simultaneously used as the input variables for training models. The results also provide a basis for selecting the time length of input variables. The wind speed is decomposed into two orthogonal one-dimensional variables in east-west and north-south directions, which prevents the method complexity increased by multi-dimensional variables. The long short-term memory neural network(LSTM) is adopted to train the prediction model for wind speed in both directions, and the prediction results are restored to wind speed and wind direction prediction data. The example results show that the proposed method can better capture the information in both wind speed and wind direction, and the prediction accuracy rate is more than 90% when the prediction errors of wind speed and wind direction are respectively less than 1.0 m/s and 5°.
Key words:  wind vector prediction method  LSTM  feature engineering  relevance  method complexity  information density

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