The invention discloses a frequency division prediction method of a photovoltaic output power value. A wavelet neural network based on grey system calibration is used, and an output trend and a characteristic of a prediction day can be extracted from historical photovoltaic data and prediction precision is increased. Decomposition is performed on historical data based on wavelet decomposition, and multiscale analysis can be performed on the historical photovoltaic data so that local information of the historical photovoltaic data is effectively extracted. A prediction model is constructed based on a neural network, which is good for extracting the output trend of a photovoltaic power station under various kinds of external conditions, and therefore, prediction precision of photovoltaic output power under a non-sunny day condition is increased. Modeling is performed on the historical data based on a gray system model, through carrying out error analysis on the historical photovoltaic data, a prediction error distribution scope is found out and a basis is provided for the gray system correction. Grey modeling is performed based on a historical data sample and further correction is performed on a prediction result of the neural network so that prediction precision of the prediction result of the neural network is increased.