中文摘要：

第一，一般回归神经网络(GRNN ) 被用于影响预报的居住负担(RL ) 的因素的钥匙的可变选择。第二， GRNN 选择的关键影响因素作为输入和城市、农村的 RL 的输出终端被使用模仿并且学习。另外，最后的模型的合适的参数通过使用证据理论联合与 PSO 方法和 Bayes 理论计算的优化结果被获得。然后， PSO-Bayes 最少的广场支持向量机器(PSO-Bayes-LS-SVM ) 的模型被建立。案例研究然后被为学习并且测试提供。城市、农村的 RL 的均方差预报的实验分析结果表演分别地是 0.02% 和 0.04% 。最后，作为一个例子在中国拿特定的省 RL，从 2011 ～ 2015 的 RL 的预报结果被获得。

英文摘要：

Firstly, general regression neural network （GRNN） was used for variable selection of key influencing factors of residential load （RL） forecasting. Secondly, the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning. In addition, the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory. Then, the model of PSO-Bayes least squares support vector machine （PSO-Bayes-LS-SVM） was established. A case study was then provided for the learning and testing. The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%, respectively. At last, taking a specific province RL in China as an example, the forecast results of RL from 2011 to 2015 were obtained.