Research On Wind Speed Prediction Model of Least Squares Support Vector Machine Through Genetic Algorithm Optimization

Wind energy has become the fastest-growing new energy source due to its environmentally friendly sustainability and has been widely used in wind power generation. Wind speed prediction is crucial to the stable operation of the power generation system. Accurately obtaining the change trend of wind speed can effectively reduce the adverse effects of wind farms on the operation of the power system. In recent years, big data technologies such as data mining and artificial intelligence have gradually become a research trend, and they have good solutions to complex nonlinear regression and classification problems. Therefore, based on machine learning and optimization algorithms, this paper combines genetic algorithm with LS-SVM, and proposes a genetic algorithm to optimize the prediction model of LS-SVM. The simulation results show that: compared with a single LS-SVM prediction model, the genetic algorithm optimized LS-SVM prediction model error is smaller and has higher prediction accuracy. This prediction method has certain practical significance.