Li Lintong, Escribano-Macias Jose, Zhang Mingwei, Fu Shenghao, Huang Mingyang, Yang Xiangmin, Zhao Tianyu, Feng Yuxiang, Elhajj Mireille, Majumdar Arnab, Angeloudis Panagiotis, Ochieng Washington
Centre for Transport Engineering and Modelling, Imperial College London, London SW7 2AZ, UK.
State Key Laboratory of Air Traffic Management System, Nanjing 210007, China.
Sensors (Basel). 2024 Sep 27;24(19):6254. doi: 10.3390/s24196254.
Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.
风速会影响航空性能、清洁能源生产及其他应用。通过准确预测风速，可以避免运行延误和事故，同时还能提高风能生产效率。本文首先概述了所有质量指标（QIs）的定义、特征、能够测量该特征的传感器，以及该特征与风速之间的关系。随后，评估了每个与风速预测相关的质量指标的特征重要性，并使用所有质量指标来预测水平风速。此外，我们还对传统的逐点机器学习模型和具有时间相关性的深度学习模型的性能进行了比较。结果表明，双向长短期记忆（BiLSTM）神经网络在三个指标上的准确率最高。此外，新提出的一组质量指标在很大程度上优于先前使用的质量指标。
