Photovoltaic Power Prediction Based on Improved Dual Parallel Process Neural Network
HE Zujun
ZHOU Guanghui
YANG Yifei
Abstract：In the field of photovoltaic power generation,the accurate prediction of photovoltaic power generation is a difficult problem in the face of complex and changeable weather. The accurate prediction of photovoltaic power generation can provide a refer?ence for the smooth dispatching of power grid. However,the traditional method of photovoltaic power output has large prediction er?ror and long response time,which is difficult to meet the demand scheduling problem of power grid. A prediction method for photo?voltaic power improved double parallel process neural network is put forward,neuron aggregation operation mechanism and incen?tive mode to the time domain are extended by the dynamic incremental update double parallel process neural network weights,in or?der to avoid the error due to small changes in the network into local minima. The simulation results show that the predicted PV power can be predicted by the improved model.
Keywords：dispatchprocess neural networkpolymerizationdynamic increment
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1890-1894 )