In order to improve the accuracy of photovoltaic power generation power prediction

the standard dung beetle optimization algorithm（DBO）was improved by using three strategies：Bernoulli mapping

the spiral update mechanism of whale optimization algorithm （WOA）and the optimal individual adaptive t-distribution. Through verification on 8 standard test functions

the results show that the improved algorithm has significant improvements in convergence speed and optimization ability. Furthermore

the improved dung beetle optimization algorithm was used to optimize the long short-term memory network model（IDBO-LSTM）for photovoltaic power generation power prediction

and compared with six other models. The prediction results show that IDBO-LSTM exhibits better prediction performance under 3 different weather types than other models. Compared with the DBO-LSTM model

the average absolute error rate（MAPE）of IDBOLSTM decreased by 0.08%

3.51%

4.02%

respectively.