Temporally Correlated Deep Learning-Based Horizontal Wind-Speed Prediction | JoVE Visualize

Area of Science:

Meteorology and Atmospheric Science
Data Science and Machine Learning
Aerospace Engineering
Renewable Energy Systems

Background:

Wind speed is a critical factor influencing aviation safety, operational efficiency, and renewable energy production.
Accurate wind speed forecasting is essential for mitigating risks like accidents and optimizing energy generation.
Existing methods for wind speed prediction often rely on traditional Quality Indicators (QIs) and point-wise models.

Purpose of the Study:

To comprehensively review the definition, characteristics, measurement sensors, and relationships of Quality Indicators (QIs) with wind speed.
To assess the feature importance of various QIs for predicting horizontal wind speed.
To compare the predictive performance of traditional machine learning models against deep learning models, specifically Bidirectional Long Short-Term Memory (BiLSTM).

Main Methods:

A detailed overview of Quality Indicators (QIs) relevant to wind speed measurement and prediction.
Feature importance analysis was conducted for each QI in the context of wind speed prediction.
Comparison of traditional point-wise machine learning models with temporally correlated deep learning models, including BiLSTM.

Main Results:

The Bidirectional Long Short-Term Memory (BiLSTM) neural network demonstrated superior accuracy in wind speed prediction across three key metrics.
A newly proposed set of Quality Indicators (QIs) significantly outperformed previously utilized QIs in prediction tasks.
Deep learning models, particularly BiLSTM, showed better performance in capturing temporal correlations compared to traditional models.

Conclusions:

The study confirms the effectiveness of advanced Quality Indicators (QIs) and deep learning models, like BiLSTM, for accurate wind speed forecasting.
The findings suggest that improved QIs and BiLSTM networks can enhance aviation safety and the efficiency of wind energy production.
This research provides a foundation for developing more robust wind speed prediction systems.