CAJ | 학술논문

提出一种基于遗传算法优化支持向量回归机的模型进行网格负载预测,使用遗传算法和交叉验证技术解决了支持向量回归机参数难以确定的问题. 标准数据集仿真实验结果表明,该模型与试验法定参的支持向量回归机和BP神经网络相比具有更优的预测性能.
제출일충기우유전산법우화지지향량회귀궤적모형진행망격부재예측,사용유전산법화교차험증기술해결료지지향량회귀궤삼수난이학정적문제. 표준수거집방진실험결과표명,해모형여시험법정삼적지지향량회귀궤화BP신경망락상비구유경우적예측성능.
A support vector regression optimized by genetic algorithm model was developed for grid host load prediction. Genetic algorithm and cross validation technology were applied to solve parameter optimization of support vector regression. Simulation experiments were performed on benchmark data set. Experimental results indicate that the proposed model exhibits better performance than support vector regression model with parameters selected by trial-and-error method and the back-propagation neural network.