The invention discloses an ultra-short-period photovoltaic prediction method. The method comprises the following steps: selecting training data x; performing normalization
processing on the training data; performing data
exception handling on the training data; performing data functional transformation; performing significance analysis; training a generalized regression neural
network model; and predicating the generalized regression neural
network model. According to the ultra-short-period photovoltaic prediction method, a generalized regression
neural network modeling theory and method is adopted; partial approximation is further accurate by adding a primary function in a
hidden layer, and
global optimum is achieved; significance extraction and improvement is carried out specific to the model input information; the correlation of historical data is enhanced through the functional transformation, and the historical data, used as the input
signal, enters the generalized regression neural network prediction model, so that the prediction efficiency is effectively improved; in addition, after a training sample is chosen, the generalized regression neural
network structure and the weight are determined automatically by only requiring to adjust
smoothing parameters, so that the computational process for
circuit training is avoided, and the global approximation study and prediction capability is realized more rapidly.