基于最小一乘的GA-SVR用电量预测

基于最小一乘准则和交叉验证思想下，提出了一种基于自适应遗传算法参数寻优的支持向量回归机模型。该模型采用最小一乘准则作为训练标准，提高了模型的整体稳定性。使用自适应遗传算法对支持向量回归模型进行参数寻优，加快了训练时间，提升了预测精度，同时，交叉验证方法的采用，又进一步地提升了模型的泛化能力和预测精度。采用该模型对江苏省全社会用电量进行预测的结果表明，其预测精度要优于传统的支持向量回归模型和一般的粒子群优化支持向量回归模型。

Predicting Electricity Consumption Based on Least Absolute Criteria of GA-SVR

The SVR model with genetic algorithm and cross validation is proposed based on least absolute criteria. In this model, training criteria is least absolute criteria, which improves the overall stability of the model. In order to speed up training time and improve prediction accuracy, the genetic algorithm is adopted to parameters optimization. At the same time, cross validation is used to enhance generalization ability and prediction precision. The research shows that this model is better than the original SVR model and PSO-SVR model in the accuracy of prediction in electricity consumption prediction of Jiangsu Province.