基于3DCNN与CLSTM混合模型的短期光伏功率预测OA

Photovoltaic(PV)power prediction is crucial for the formulation of power generation plans and coordinated dispatch in the power system.However,due to the stochastic and intermittent nature of PV generation,there is still significant room for improvement in the accuracy of PV power prediction.Therefore,a hybrid model based on a three-dimensional convolutional neural network(3DCNN)and convolutional long short-term memory network(CLSTM)for PV power prediction was proposed,which combines the strengths of the 3DCNN and CLSTM models to enhance prediction accuracy.The prediction model using three metrics:root mean square error(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE)was evaluated.By applying the prediction method based on the hybrid model to the output prediction of a specific PV plant,the applicability and correctness of the model were validated.The results show that when the input time series of solar irradiance,temperature,humidity,wind speed,etc.are the same,the hybrid model based on 3DCNN and CLSTM achieves the best prediction performance.Compared to the individual 3DCNN model,CLSTM model,and backpropagation neural network(BPNN)model,the hybrid model improves MAPE by 54%,61%,and 64%,respectively.It was indicated that the hybrid model can better adapt to the stochastic and intermittent characteristics of PV generation,thus improving the accuracy of power prediction.