Photovoltaic Power Generation Prediction Based on In-Depth Learning for Smart Grid

With the continuous development of photovoltaic power generation technology, the problems of intermittence and randomness of photovoltaic power generation become prominent. Therefore, the connection of the photovoltaic system to the grid will impact the stability of the power system and power dispatching. If the photovoltaic power generation can be accurately predicted, it will improve the coordination of power generation of the photovoltaic system and the stability of the power grid after the system grid connection. In a photovoltaic system, there are many factors affecting photovoltaic power, and there are different algorithms for power prediction. In this paper, long short-term memory (LSTM) is used to predict the power generation of the photovoltaic power system. LSTM can learn the correlation features of the time series data without the problems of data gradient disappearance of the traditional recurrent neural network algorithm. The prediction results are then directly applied to the existing integrated photovoltaic power storage system. Through the experiments, it is verified that the prediction accuracy can reach higher than 98%.