摘要
为了减轻风电对电网的影响,降低供电系统的旋转备用容量和运行成本,提出了以混沌理论为基础,基于相空间重构的支持向量机短期风速预测方法。为提高预测模型的预测精度和泛化能力,利用粒子群算法选择对相空间重构和支持向量机参数联合寻优,将最佳参数代入混沌支持向量机模型对短期风速进行预测。试验结果表明了该方法的有效性。
In order to reduce the influence of wind power to power system, reduce the rotating spare capacity and operation cost of power supply system, a method of wind power prediction based on chaotic theory and support vector machine （ C-SVM ） was proposed. A method was developed for jointly optimization of phase space reconstruction and support vector machine parameters, using the interdependent relationship between phase space reconstruction and support vector machine parameters to improve the model prediction performance. The optimized parameters were put into C-SVM model to forecast the short-term wind speed. The simulated results show that the proposed method achieves perfect accuracy and efficiency in short-term wind speed forecasting.
出处
《低压电器》
2012年第16期48-52,共5页
Low Voltage Apparatus
基金
河北省科技支撑计划项目(10215682
10213901D)
河北省建设科技研究计划项目(2011-147)
关键词
风电
支持向量机
短期风速预测
混沌特性
相空间重构
wind power
support vector machine（SVM）
short-time wind speed forecasting
chaoticproperty
phase-space reconstruction
