Short-term Prediction Method Of Wind Power Based On Improved Support Vector Machine

By 2022,China’s wind and photovoltaic power generation accounted for 11% of the total social electricity consumption.As the representative of new energy,wind power generation has been widely used,which effectively alleviates the situation of power resource shortage,reduces power generation expenditure,reduces greenhouse gas emissions and improves social benefits,and meets the fundamental needs of China’s power reform.It is an inevitable choice to help China’s rapid economic development.However,there are also many natural defects in wind power generation,such as random volatility and anti-peaking characteristics,which will impact the security and stability of the grid.When wind power is integrated into the grid,the peakvalley difference of grid load gradually increases,which increases the difficulty of power dispatching and deepens the contradiction of peak shaving.With the increasing scale of wind power grid connection,more accurate wind power prediction is conducive to improving the acceptance capacity of the grid for wind power.Therefore,putting forward a feasible and effective wind power prediction method has become a hot topic in the industry.In view of the low accuracy of the current wind power prediction model,this paper proposes a short-term wind power prediction model based on IALO-LS-SVM.Compared with the new model,the proposed model has a lower prediction error of the output results,which shows a better prediction ability for wind power.The main work of this paper is as follows:(1)Propose a wind power prediction method based on Ant Lion Optimization(ALO)to optimize Support Vector Machine(SVM).In response to the non-linear and large-scale characteristics of wind speed and wind power data,this article selects support vector machines as the basic model for wind power prediction;In response to the problem that the selection of support vector machine parameters relies on manual experience,this article uses the ant lion algorithm to achieve adaptive optimization of support vector machine parameters.The results show that the ant lion algorithm has advantages in improving the accuracy of support vector machine for wind power prediction.(2)Propose a wind power prediction method based on ant lion algorithm optimized Least Squares Support Vector Machine(LS-SVM).Aiming at the problem of high model complexity and low prediction accuracy when processing wind power related data with large-scale samples using support vector machines,an improvement strategy based on least squares support vector machines is proposed as a wind power prediction model;In response to the problem that the selection of parameters for least squares support vector machines relies on artificial experience,this paper uses the ant lion algorithm to achieve adaptive optimization of least squares support vector machine parameters.The results demonstrate the effectiveness of the replacement model strategy.(3)Propose a wind power prediction method based on improved ant lion algorithm optimized least squares support vector machine.In response to the occasional problem of low optimization accuracy in the ant lion algorithm,chaos mapping,Levy flight strategy,and particle swarm optimization algorithm were selected to improve it.Through example analysis,it was proven that the improved strategy improved the optimization accuracy and convergence speed of the ant lion algorithm.The experimental results demonstrate that the proposed method can effectively predict wind power.