Small-Sample Short-Term Photovoltaic Output Prediction Model Based on GRA-SSA-GNNM Method

The precision of photovoltaic (PV) output forecasting results is crucial to the reliability of the intelligent distribution network and multi-energy supplementary system. This work aims to address problems of insufficient research related to the short-term prediction of small-sample PV power generation and the low prediction accuracy in the previous research. A hybrid prediction model based on grey relation analysis (GRA) combined with the sparrow search algorithm (SSA) and the grey neural network model (GNNM) is proposed. In this paper, GRA is utilized to reduce the dimension of meteorological features of the samples. Then, the GNNM is used to perform regression analysis on the input features after reducing the dimension of meteorological features of the samples, and the parameters of the GNNM are optimized via SSA. A limited dataset was used to compare several models in different seasons and weather conditions. The prediction results agree well with the data from the PV power plant in Xinjiang, indicating that the GRA-SSA-GNNM model developed in this work effectively achieves a high precision estimation in short-term PV power generation output prediction and has a promising application in this field.