基于改进粒子群算法优化支持向量机的风电功率预测

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Abstract Wind power generation is one of the key points in the development of new energy. Accurate wind power prediction directly affects the stability of power grid, so it is necessary to study wind power prediction. Aiming at the problem of low prediction accuracy, an improved particle swarm optimization (IPSO) algorithm is putting forward to optimize the prediction method of support vector machine. As the penalty factor and kernel function parameter selection of support vector machine have a great influence on the prediction accuracy, Therefore, the improved particle swarm optimization algorithm is used to optimize the parameters of the support vector machine, And then they are used for modeling training, the built model is applied to the wind power prediction, and finally the results are analyzed. The prediction results show that the improved particle swarm optimization optimized support vector machine has better accuracy for wind power prediction.

Key words： particle swarm optimization (PSO) parameter optimization support vector machine (SVM) prediction algorithm

Received: 09 May 2020

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	Articles by authors
	Zhao Qian
	Chen Fangfang
	Gan Lu

Cite this article:

Zhao Qian,Chen Fangfang,Gan Lu. Wind power prediction based on support vector machine trained by improved particle swarm optimization[J]. Electrical Engineering, 2020, 21(12): 12-16.

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http://dqjs.cesmedia.cn/EN/Y2020/V21/I12/12

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