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.