A robust spatial-temporal prediction model for photovoltaic power generation based on deep learning

The accurate spatial-temporal prediction of photovoltaic (PV) power generation helps the power system dispatching department to make reasonable dispatching plans. In this paper, a robust spatial-temporal prediction model for PV power generation based on a denoising autoencoder (DAE) combining Gramian angular summation fields (GASF) and convolutional neural network (CNN) is proposed. First, the downscaling and modeling of power data use Pearson correlation to reduce inaccurate and inefficient predictions due to large-scale sparse data. Second, the model training data are encoded with noise reduction using a denoising autoencoder. Finally, the performance of the proposed model is experimentally verified. The results show that the model still performs well when the data exist in different degrees of contamination, with an average mean square error (MSE) of 71.58. Experiments based on China and the USA PV power generation datasets with mean absolute error (MAE) of 4.26 and 3.96, respectively.