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LSSVM (least squares support vector machine) wind speed forecasting method based on integration of GA (genetic algorithm) and PSO (particle swarm optimization)

A genetic algorithm and wind speed prediction technology, applied in the field of LSSVM wind speed prediction based on genetic algorithm and particle swarm integration, can solve the problem of unsatisfactory prediction accuracy and speed of the prediction model

Inactive Publication Date: 2015-09-30
SHANGHAI UNIV
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Problems solved by technology

At present, the common ways to optimize LSSVM mainly include particle swarm algorithm, genetic algorithm, ant colony algorithm and artificial bee colony algorithm. To a certain extent, various optimization algorithms have achieved certain results in optimizing LSSVM parameters, but the obtained The prediction accuracy and speed of the prediction model are still not ideal

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  • LSSVM (least squares support vector machine) wind speed forecasting method based on integration of GA (genetic algorithm) and PSO (particle swarm optimization)
  • LSSVM (least squares support vector machine) wind speed forecasting method based on integration of GA (genetic algorithm) and PSO (particle swarm optimization)
  • LSSVM (least squares support vector machine) wind speed forecasting method based on integration of GA (genetic algorithm) and PSO (particle swarm optimization)

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[0044] The implementation of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0045] The present invention adopts the LSSVM whose kernel function is a radial basis function, and then applies the method of integrating GA and PSO to quickly select the best combination of kernel function parameter σ and regularization parameter C. The genetic algorithm starts to search from the string set, which has a large coverage and strong global optimization ability, but it is easy to converge prematurely and fall into a local optimum; the particle swarm algorithm uses the fitness value to evaluate the system, and conducts a certain random search according to the fitness value. It is not sensitive to the initialization of the population, the search speed is fast, and the local search ability is strong. Therefore, the genetic algorithm and the particle swarm optimization are combined, and the genetic algorithm is used for global ...

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Abstract

The invention provides an LSSVM (least squares support vector machine) wind speed forecasting method based on integration of GA (genetic algorithm) and PSO (particle swarm optimization). The method comprises the following steps: finite wind speed samples are divided into a training set and a testing set, and normalization processing is performed; GA and LSSVM related parameters are initialized; chromosome coding is performed, and initial population is generated randomly; the fitness corresponding to each chromosome is calculated, if requirements are met, the PSO in the fifth step is started directly, and if the requirements are not met, selection, crossover and mutation operation of the GA are performed; optimum parameter combination obtained with the GA is used for initializing the PSO related parameters; the optimum position fitness value of each particle is compared with the optimum position fitness value of the swarm; the final optimum parameter combination is output, and an optimized LSSVM model is obtained; a forecast wind speed time history spectrum is obtained. The LSSVM wind speed forecasting method based on integration of GA and PSO has the characteristics of high optimization precision, high convergence precision, fewer iterations, high success rate and the like.

Description

technical field [0001] The invention relates to a wind speed prediction method based on intelligent optimization and integration of LSSVM (least squares support vector machine), specifically an LSSVM fluctuating wind speed prediction method based on genetic algorithm (GA) and particle swarm (PSO) integration. Background technique [0002] For high-rise structures, long-span space structures, and high-voltage transmission tower systems, wind loads are an important type of random dynamic loads that must be considered in structural design. Improper design of wind load will not only affect the comfort level of people using the building structure, but also cause certain damage and damage to the building structure, which will bring huge loss of life and property to people. Therefore, it is extremely important to consider the dynamic response of wind in engineering, and it has strong engineering practical significance to realize the accurate prediction of wind speed. [0003] Supp...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12G06K9/62G06Q10/04
Inventor 李春祥丁晓达
Owner SHANGHAI UNIV
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