Effective short-term prediction of regional voltage load is of great significance to the implementation of energy saving and emission reduction policies in China.Accurate prediction of real-time demand voltage can red...Effective short-term prediction of regional voltage load is of great significance to the implementation of energy saving and emission reduction policies in China.Accurate prediction of real-time demand voltage can reduce power waste and carbon emissions,make outstanding contributions to delaying global climate warming,and is conducive to global environmental protection and sustainable development.On the short-term load forecasting of power system,a variant model of RNN-LSTM is tested in this paper.It effectively solves the problem of gradient explosion and disappearance caused by large amount of data input in classical RNN.On the basis of this model,optimization experiments are carried out under different super parameters to achieve better prediction results.The experimental results show that the accuracy of test set reaches 99.8%,which proves that the method proposed in this paper has certain reference value.展开更多
基金Supported by Natural Science Foundation of Hunan Province(2020JJ4306)"Scientific Innovation Plan"of the Chinese Academy of Sciences(20194001882)。
文摘Effective short-term prediction of regional voltage load is of great significance to the implementation of energy saving and emission reduction policies in China.Accurate prediction of real-time demand voltage can reduce power waste and carbon emissions,make outstanding contributions to delaying global climate warming,and is conducive to global environmental protection and sustainable development.On the short-term load forecasting of power system,a variant model of RNN-LSTM is tested in this paper.It effectively solves the problem of gradient explosion and disappearance caused by large amount of data input in classical RNN.On the basis of this model,optimization experiments are carried out under different super parameters to achieve better prediction results.The experimental results show that the accuracy of test set reaches 99.8%,which proves that the method proposed in this paper has certain reference value.
