Application of support vector machine model in wind power prediction based on particle swarm optimization

Wind energy is a kind of renewable and clean energy, andwind power is a non-hydropower renewable energy which has the best technicaland economic conditions for large-scale development. It is characterized byfluctuation, intermittency, low energy density, etc., so wind power is alsofluctuating. When a large-scale wind farm is connected to a power grid,great fluctuation in wind power will cause adverse effect to the powerbalance and frequency adjustment of the power grid. If the generation powerof the wind farm can be prediction, the electricity dispatch department canarrange dispatch plans in advance according to the change in wind power andbetter protect the power balance and operation safety of the power grid. Inthis article, a SVM model is used to predict wind power and modified PSO isused to optimize SVM parameters, realizing the optimized selection of theSVM model parameters, which makes such prediction more close to actual law.Actual calculation examples shows that the prediction method used in thearticle has good convergence, high prediction precision and actualapplication value.