Wind Data Pattern and Trend Analysis Using Feature Identification and Extreme Wind Speed Prediction

Optimization Techniques for Hybrid Power Systems

1. Introduction

Wind disasters are described as weather conditions where there is severe destruction due to strong wind. Hurricanes, tornadoes, thunderstorms, dust storms, and windstorms are a few examples of wind disaster types. There are specific characteristics of the occurrence that must be observed to determine the type of wind hazard. For instance, a hurricane has a center of low pressure, powerful winds that blow in a round pattern, and torrential rain. A funnel cloud stretching from the base of a thunderstorm and a strong, rotating column of air are two characteristics of a tornado. A thunderstorm is a storm that includes thunder, lightning, a lot of rain, and strong winds. Finally, a windstorm is a storm that has powerful, destructive winds that can wreak havoc. One can determine the sort of wind by checking for these characteristics. To decrease the risk of wind disasters, it is important to be able to predict wind disasters and forecast extreme wind speeds. Machine learning algorithms can be used to examine data from many sources to identify and predict wind disasters. To determine the type of wind disaster, such as a tornado or hurricane, feature identification can be performed to collect meteorological data such as wind direction, air pressure, precipitation, and wind speed. There are many techniques for extraction, including SVM’s recursive feature removal method, the algorithm for empirical mode decomposition, and recursive algorithms. Machine learning algorithms, such the Ada boost classification approach, Support Vector Machine (SVMs), neural network, Logistic Regression (LR) and Random Forest (RF) can be used to predict the intensity and speed of the wind once the wind disaster has been identified. Analyzing the data and looking for patterns or trends can help with this. An algorithm can predict the possibility of an extreme wind speed occurrence by identifying these patterns. This data can be used to inform decisions regarding evacuation or the execution of safety precautions. Additionally, the suggested strategy can be adjusted for other locations and wind disaster types.

The remaining section of the paper is organized as follows. Section 2 presents the related work on Wind data pattern and trend analysis for feature identification and extreme wind speed prediction. Section 3 discusses the proposed techniques on Wind data pattern and trend analysis for feature identification and extreme wind speed prediction. Section 4 discusses the experiments and results demonstrate and its working techniques. Section 5 concludes the paper with significant future directions.