最小二乘支持向量机在短期风速预测中的应用概况

Application Profiles of Least Support Vector Machine in Short-term Wind Speed Forecasting

从最小二乘支持向量机(LS-SVM)的原理出发,从本质上阐明了LS-SVM在短期风速预测中的可行性与优越性。在对LS-SVM在应用中存在的包括数据预处理、核函数构造及选取以及参数优化等问题进行分析后,归纳了现行主要解决方法,从而全面总结了LS-SVM在短期风速预测中的应用概况。最后对基于LS-SVM的短期风速预测所存在的问题进行总结,并提出建议。

Based on the principle of LS-SVM, the feasibility and superiority of the LS-SVM method of Short term wind speed forecasting are essentially clarified. Some problems about the application of LS-SVM, including data pre-processing, the constructing and current solutions are provided respectively. For a series of LS-SVM-based improvements and some combination forecasting methods consisting of LS-SVM with other algorithms, a comprehensive summary is given, from the perspective of the mechanism about LS-SVM algorithm being applied to Short term wind speed forecasting, and the elevation of prediction accuracy and speed. Finally, some key issues about LS-SVM-based Short term wind speed forecasting are summarized and some recommendations are given.