Small sample load prediction method based on hybrid neural network and generative adversarial

The invention discloses a small sample load prediction method based on a hybrid neural network and generative adversarial. The method comprises the following steps: collecting historical user power consumption data and corresponding weather and date characteristics; constructing a load characteristic input matrix; extracting high-dimensional load characteristics of historical users and newly-added users, and sending the high-dimensional load characteristics into a load prediction neural network for pre-training; fixing parameters of the load feature extractor in the source domain, constructing a load feature extractor in the target domain, and updating parameters of the load feature extractor in the target domain; and connecting the target domain feature extractor G2 obtained by adversarial training with a pre-trained load prediction neural network, and applying to load prediction of newly added users in the target domain. Through knowledge migration among different fields, a large amount of historical load k