电气技术  2020, Vol. 21 Issue (12): 12-16    DOI:
研究与开发 |
基于改进粒子群算法优化支持向量机的风电功率预测
赵倩, 陈芳芳, 甘露
云南民族大学电气信息工程学院,昆明 650504
Wind power prediction based on support vector machine trained by improved particle swarm optimization
Zhao Qian, Chen Fangfang, Gan Lu
Yunnan Minzu University, Kunming 650504
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摘要 风力发电是新能源发展的重点之一,准确的风力发电功率预测直接影响着电网的稳定性,所以研究风电功率预测十分必要。本文针对预测精度不高的问题,提出了一种改进的粒子群算法优化支持向量机的预测方法。由于支持向量机的惩罚因子和核函数参数选择对预测精度有很大影响,因此利用改进的粒子群算法对支持向量机参数进行寻优,用优化好的参数进行建模训练,然后把建好的模型应用于风电功率预测,最后对结果进行分析。预测结果表明:改进粒子群算法优化的支持向量机对风力发电功率预测有更好的准确性。
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关键词 粒子群算法参数优化支持向量机预测算法    
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 wordsparticle swarm optimization (PSO)    parameter optimization    support vector machine (SVM)    prediction algorithm   
收稿日期: 2020-05-09     
作者简介: 赵 倩(1993-),女,河南省焦作市人,硕士研究生,主要研究方向为风电短期出力预测。
引用本文:   
赵倩, 陈芳芳, 甘露. 基于改进粒子群算法优化支持向量机的风电功率预测[J]. 电气技术, 2020, 21(12): 12-16. Zhao Qian, Chen Fangfang, Gan Lu. Wind power prediction based on support vector machine trained by improved particle swarm optimization. Electrical Engineering, 2020, 21(12): 12-16.
链接本文:  
https://dqjs.cesmedia.cn/CN/Y2020/V21/I12/12