Abstract To maximize power generating efficiency, accurate forecast of generation power is the very important job for the photovoltaic generation system. In this paper, photovoltaic production is forecasted using a understanding way called multi-scale based on convolutional neural network-long short-term memory network (CNN-LSTM) for photovoltaic output prediction of multi-scale method. However, existing methods are often challenged by the multi-scale nature of the data when dealing with PV output prediction, so a more efficient method is needed to overcome difficulties. Therefore, this paper put forward an idea of a multi-scale method based on CNN-LSTM, which combines CNN and LSTM to accurately predict photovoltaic output at different time scales. The CNN-LSTM model can achieve the precision of 0.9097 for 15 minutes, the prediction accuracy of 0.8750 for 1 hour, and the prediction accuracy of 0.7987 for 4 hours, which can prove that the model can meet the scheduling rules.