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The effectiveness of photovoltaic power generation systems—a clean and renewable use of solar energy—depends on the amount of solar radiation. Consequently, solar radiation forecasting (especially short-term) is crucial for photovoltaic plants. In this paper, a hybrid convolutional neural network (CNN) and gate recurrent unit (GRU) method is proposed for short-term (10-min) solar radiation forecasting based on image and time-series data (i.e., radiation data at different times). The method aims to achieve high performance in solar radiation forecasting, which can be useful for PV plant adjustment. CNN–GRU consists of two branches. One is based on the ResNet-18 structure, which can extract features from sky images. The other is a GRU branch, which consists of three fully connected layers used for meteorological feature extraction. Experiments on a public dataset showed that our method predicts the mean absolute error better than other benchmark models. The ablation experiments demonstrated that the hybrid model performs better than a single model and, therefore, shows promise for application in solar radiation forecasting.

Short-Term Global Horizontal Irradiance Forecasting Using a Hybrid Convolutional Neural Network-Gate Recurrent Unit Method