To ensure the stability of power grid operation and improve the economic benefits of photovoltaic power plants

the forecast accuracy of distributed photovoltaic power output needs to be further improved. Thus

a distributed photovoltaic power prediction method based on the combination of spatiotemporal information is proposed. Firstly

on the basis of XGBoost-LSTM integrated model

the time series of photovoltaic historical data are used to carry out the prediction. Then

by using the LSSVM model

the spatial correlation between photovoltaic power plants is used to conduct the prediction. Finally

according to the basisc principle of information entropy

and combing with the information entropy improved by multi-error evaluation criteria

the weight of the two prediction methods is determined

and the combined prediction model is constructed to realize spatiotemporal information combination prediction. The research result shows that

the mean absolute error of the XGBoost-LSTM + LSSVM model based on the information entropy improved by multi-error evaluation criteria decreases by 1.6% and 8.3%respectively

compared with the combined model based on cross entropy and the model based on information entropy. Combining with the advantages of a single forecasting model

the combined models can reduce the forecasting error. The information entropy combination model based on multi-error evaluation criteria has higher robustness and comprehensive performance

and can improve the accuracy of distributed photovoltaic power prediction.