Wind Speed Prediction Using Support Vector Machines

Support Vector Machine (SVM) is a machine learning algorithm widely used for regression and classification problems, demonstrating significant advantages in wind speed prediction. Since wind speed is influenced by multiple meteorological factors such as temperature, air pressure, and terrain, traditional methods struggle to achieve accurate modeling. SVM effectively handles nonlinear relationships through kernel functions, improving prediction accuracy and subsequently enhancing the reliability of wind power forecasting.

In wind power generation, wind speed directly impacts power generation efficiency. When using SVM for wind speed prediction, historical wind speed data and other relevant meteorological indicators are typically collected as input features. SVM fits the data by finding the optimal hyperplane, where support vectors determine the model's generalization capability. Common kernel functions like Radial Basis Function (RBF) adapt well to complex nonlinear patterns in wind speed data.

Compared to traditional time series models (e.g., ARIMA), SVM's advantages lie in its robustness to noisy data and ability to handle high-dimensional features. Combining cross-validation and grid search for hyperparameter optimization can further enhance prediction accuracy, providing reliable foundations for wind farm operation scheduling.

Improvements in wind speed prediction directly reduce uncertainties in wind power grid integration, contributing to grid stability. Future work could explore hybrid prediction methods combining deep learning models for higher precision forecasting.