Abstract: In order to reduce the error of power prediction values for distributed photovoltaic access distribution networks,a distributed photovoltaic access distribution network power prediction method considering information time shift is proposed.Using Pearson correlation coefficient to describe the correlation between key meteorological factors and photovoltaic output power of NWP,determine the optimal time shift,and correct the meteorological information offset caused by geographical location;using random forest algorithm to process distributed photovoltaic output data and screen high contribution feature parameters;after learning feature parameters using a bidirectional GRU neural network,each time step of the input time series is captured using a one-dimensional convolutional neural network;introducing attention mechanism to reduce the impact of meteorological time shift on distributed photovoltaic output and achieve the output of power prediction results.The experimental results show that this method can calculate the Pearson correlation coefficient between distributed photovoltaic output and meteorological data,and determine its optimal time shift;the error between predicted values and actual values is relatively small under different meteorological conditions.

Key words: Information time shift, Distributed photovoltaic, Distribution network, Power prediction