电力系统保护与控制2011，Vol.39Issue(11)：44-48,61,6.
基于遗传优化的最小二乘支持向量机风电场风速短期预测
Forecast of short-term wind speed in wind farms based on GA optimized LS-SVM
杨洪 1古世甫 1崔明东 2孙禹2
作者信息
- 1. 西华大学电气信息学院,四川,成都,610039
- 2. 中国华电集团公司云南以礼河发电厂,云南,会泽,654200
- 折叠
摘要
Abstract
Timely and effective information can be obtained and then applied to the planning, scheduling, operation and control of wind power system, provided that the short-term wind speed can be accurately forecasted in wind farms.Support vector machine algorithm is established based on structural risk minimization principles.It considers smoothness of the regression curve entirety on the whole in regression model and predicts wind speed and tracks trend in time.To sovle the problem that the parameters of SVM are difficult to determine, genetic algorithm is employed to optimize the penalty factor C and kernel parameter σ2 of support vector machines.In the genetic coding of the parameters, the search sensitivity is improved and the model convergence speed is accelerated through the logarithmic transformation.Finally, prediction of the last 12-hour samples of 150-hour wind speed samples is done, and compared with the general regression neural network (GRNN) method, LS-SVM achieves better generalization ability and its average absolute value of relative error is only 8.32％.关键词
遗传算法/支持向量机/参数优化/短期风速预测Key words
genetic algorithm/ support vector machine/ parameter selection/ short-term wind speed forecasting分类
信息技术与安全科学引用本文复制引用
杨洪,古世甫,崔明东,孙禹..基于遗传优化的最小二乘支持向量机风电场风速短期预测[J].电力系统保护与控制,2011,39(11):44-48,61,6.
