电子测量与仪器学报

魏昱洲,许西宁.基于LSTM长短期记忆网络的超短期风速预测[J].电子测量与仪器学报,2019,33(2):64-71

基于LSTM长短期记忆网络的超短期风速预测

Ultra short term wind speed prediction model using LSTM networks

DOI：

英文关键词:LSTM ultra short term prediction wind speed prediction

基金项目:“十三五”国家重点研发计划 (2016YFB1200401)、载运工具先进制造与测控技术教育部重点实验室（北京交通大学）开放课题资助项目

作者	单位
魏昱洲	1.北京交通大学机械与电子控制工程学院
许西宁	1.北京交通大学机械与电子控制工程学院,2.北京交通大学载运工具先进制造与测控技术教育部重点实验室

Author	Institution
Wei Yuzhou	1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University
Xu Xining	1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University ,2. Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology,Ministry of Education, Beijing Jiaotong University

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中文摘要:

大风天气容易导致高速列车发生脱轨、翻车等事故，因此对于风速的超短期预测对于高铁安全行驶具有重要的意义。提出一种基于长短期记忆（LSTM）网络的预测模型，对WindLog风速传感器采集得到的每分钟最大风速数据进行分组预处理，设置合理的步长参数，建立双层LSTM网络结构，采用北京市海淀区的风速数据进行训练，并对超前1、5、10 min的风速进行超前预测，超前1 min的预测值平均误差为0467 m/s，正确率达100%；超前5 min的预测值平均误差为0543 m/s，正确率达996%；超前10 min的预测值平均误差为0627 m/s，正确率达988%。实验结果表明，该预测模型具有较好的适应性和较高的预测精度。

英文摘要:

Gale weather can easily cause high speed train accidents such as derailment and rollover. Therefore, the ultra short term prediction of wind speed is of great significance for the safe operation of high speed rail. A prediction model based on long short term memory (LSTM) networks is proposed in this paper. The maximum wind speed data per minute collected by WindLog wind speed sensor is pre processed. The proposed model was trained using wind speed data of Haidian District, and the wind speeds 1, 5 and 10 min ahead were predicted. The mean absolute error (MAE) of 1min ahead prediction was 0467 m/s with the accuracy rate of 100%. The MAE of 5 min ahead prediction is 0543 m/s with the accuracy rate of 996%, the MAE of 10 min ahead prediction is 0627 m/s, and the accuracy rate was 988%. The experimental results show that the prediction model has better adaptability and higher prediction accuracy.

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