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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.
Firstly, general regression neural network (GRNN) was used for variable selection of key influencing factors of residential load (RL) forecasting. Secondarily, the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning. 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. Chen, 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.