Research on Short-Term Hydropower Power Prediction Model Based on PSO-SVM
For short-term hydropower power impact analysis,in order to improve the accuracy of hydropower power prediction,Particle Swarm Optimization was used to optimize the parameters of support vector machines(SVM).The optimal prediction model is established and the hydropower power is forecasted.Firstly,SVM model is used to classify and identify hydropower power characteristics.Secondly,PSO was introduced to the SVM optimization algorithm model to optimize its parameters and improve the classification recognition accuracy of the model.The experimental results show that the PSO-SVM model can significantly improve the accuracy and efficiency of hydropower power prediction,and has certain practical value.
hydropower power forecastparticle swarm optimization algorithmsupport vector machine
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