中文摘要：

基于不平的集合(RS ) 预报模型的一台支持向量机器(SVM ) 数据 preprocess 被联合不平的集合属性减小和支持向量机器回归建议算法，因为在二个模型之间有强壮的补充。第一，不平的集合被用来减少条件属性，然后，它采用了减小和相应原来的数据获得到再形成的最小的条件属性消除第二，为预报是冗余的属性一件新训练样品，它仅仅让了重要属性影响预报精确性。最后，它在减小以后与训练样品学习了并且训练，由最小的条件属性和相应原来的数据输入了测试样品 re-formed，然后在测试它以后，得到了在条件属性和预报变量之间的印射的关系模型 SVM。这个模型被用来预报电源供应和需求。结果证明平均绝对错误整个社会和每年最大的负担的电源消费评价分别地，是 14.21% 和 13.23% 它显示 RS-SVM 预报模型有精确性的更高的度。

英文摘要：

A support vector machine （SVM） forecasting model based on rough set （RS） data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there are strong complementarities between two models. Firstly, the rough set was used to reduce the condition attributes, then to eliminate the attributes that were redundant for the forecast, Secondly, it adopted the minimum condition attributes obtained by reduction and the corresponding original data to re-form a new training sample, which only kept the important attributes affecting the forecast accuracy. Finally, it studied and trained the SVM with the training samples after reduction, inputted the test samples re-formed by the minimum condition attributes and the corresponding original data, and then got the mapping relationship model between condition attributes and forecast variables after testing it. This model was used to forecast the power supply and demand. The results show that the average absolute error rate of power consumption of the whole society and yearly maximum load are 14.21% and 13.23%, respectively, which indicates that the RS-SVM forecast model has a higher degree of accuracy.