Temporally Correlated Deep Learning-Based Horizontal Wind-Speed Prediction - Lintong Li; Jose Escribano-Macias; Mingwei Zhang; Shenghao Fu; Mingyang Huang; Xiangmin Yang; Tianyu Zhao; Yuxiang Feng; Mireille Elhajj; Arnab Majumdar; Panagiotis Angeloudis; Washington Ochieng (Sensors, 2024)
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.