Short-term photovoltaic output probability prediction method based on simplest gated neural network

The invention relates to a short-term photovoltaic output probability prediction method based on a simplest gated neural network, and the method comprises the following steps: 1), carrying out the normalization of original data containing a plurality of to-be-selected weather variables, and carrying out the reduction of the dimension of the original data through employing a maximum information coefficient MIC; 2) dividing the reduced feature data set into a training data set and a test data set, and respectively dividing the training data set and the test data set into four weather type data of sunny days, cloudy days, cloudy days and rainy days by adopting a K-means algorithm; 3) constructing a neural network quantile regression model and performing training by adopting the training dataset; and 4) performing prediction by adopting the trained neural network quantile regression model to obtain quantiles under various conditions, and obtaining an approximately complete probability density function through ker