Wind power plant wind speed prediction method based on support vector machine

Abstract

The invention belongs to the field of wind energy prediction, and particularly relates to a wind power plant wind speed prediction method based on a support vector machine. A continuous prediction method and a neural network algorithm are adopted, a wind speed prediction value of a mixed algorithm based on a time sequence and Kalman filtering is taken as input, a practical wind speed value is taken as output, a linear combination prediction model is established, and the linear combined prediction model is taken as a reference to analyze the prediction performance of the combined prediction model based on a least squares support vector machine. The prediction performance of each model adopts three error indexes including a prediction average absolute error, an average square error and an average absolute percentage error to carry out comparison analysis. Wind speed data is simulated to carry out simulation, each model is used for carrying out short-term prediction on the wind speed, and the effectiveness of the method is proved. A simulation experiment indicates that the wind speed prediction accuracy can be further improved by the combined prediction model. Compared with a traditional linear combined prediction model, the combined prediction model based on the least squares support vector machine has a large accuracy advantage.

Description

technical field [0001] The invention belongs to the field of wind energy prediction, in particular to a wind speed prediction method of a wind farm based on a support vector machine. Background technique [0002] In today's increasingly serious energy crisis, the development and utilization of new energy has received more and more attention. Among these new energy sources, wind power has been widely used. Recently, many domestic research results have been obtained on wind energy forecasting related topics. Although there are not many studies on wind speed or wind power prediction of wind farms, a few institutional scholars have begun to do so. Most of these institutions are related to the power system, and they are doing research in some general directions. The randomness of wind power has brought a series of problems to the operation of the power system. When large-scale wind farms are integrated into the grid, it will bring a great burden to the security and stability o...

AI Technical Summary

Problems solved by technology

Among them, the wind speed prediction is an indirect prediction method of wind power. By predicting the wind speed of the wind farm, the randomness of wind power can be reduced, thereby effectively alleviating the adverse impact of the wind farm on the power system. However, the prediction based on a single wind speed prediction model The accuracy has the defect of limited room for improvement

Images