Wind power prediction method for optimizing LSSVR based on improved satin blue kingpin algorithm

Abstract

The invention belongs to the technical field of wind power, and particularly relates to a wind power prediction method for optimizing LSSVR based on an improved satin blue kingpin algorithm. The invention provides an LSSVR wind power ultra-short-term prediction method based on secondary decomposition and ISBO optimization parameters, the complexity of a model training process and prediction is considered, a secondary decomposition strategy based on secondary decomposition and t mean value test is constructed, and decomposition, dynamic division and reconstruction processing are carried out on an original wind power sequence; in order to improve the population diversity and algorithm convergence degree of the satin blue katingbird algorithm in the iteration process, the standard satin blue katingbird algorithm is improved, the improved satin blue katingbird algorithm is utilized, two parameters of a least square support vector regression model are optimized, and a wind power LSSVR prediction model with excellent performance is established.

Description

technical field [0001] The invention belongs to the technical field of wind power, and in particular relates to a wind power prediction method based on an improved satin blue bower bird algorithm to optimize LSSVR. Background technique [0002] With the rapid development of the wind power market, the installed capacity of wind power and wind power generation continue to increase. Due to the random fluctuation of wind power, large-scale wind power grid integration has a serious impact on the safety, controllability and economy of the power system. By establishing a high-performance wind power forecasting system, accurate forecasting of wind power in the future can be achieved, which can effectively promote the stable connection of large-scale wind power to the power network. Considering that historical wind power data is a time series with fluctuation characteristics, combining signal decomposition technology with machine learning and applying it to wind power forecasting ca...

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to propose a super-short-term prediction method of LSSVR wind power based on secondary decomposition and ISBO optimized parameters, which can effectively solve the problem that parameters are difficult to determine when using least square support vector regression to establish a wind power prediction model , and using the quadratic decomposition method to deeply mine the time series characteristics of wind power, the existing problems of model training and prediction complexity

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