The penetration rate of distributed photovoltaic power stations in the power system is increasing year by year

to ensure the safe and stable operation of the power grid

a distributed photovoltaic ultra-short-term power prediction method based on combined neural networks is proposed. Firstly

a 1DCNN1DCNN-LSTM combined neural network model is constructed by using 1D convolutional neural network(1DCNN) and long short-term memory(LSTM) neural networks

to obtain multi location numerical weather prediction(NWP) information and historical power information

using combined neural network model for spatially correlated photovoltaic power prediction and time series prediction; and a fully connected neural network(FCNN) is added to the combined neural network model

which is used to learn and assign weights to the two prediction results

achieving ultra-short-term prediction of distributed photovoltaic power generation. The validation was conducted using measured data from a photovoltaic power station in Hebei

and the results showed that this method can effectively improve the accuracy of distributed photovoltaic prediction and has certain practical value.