Federal learning load prediction method based on dynamic weighted aggregation

The invention relates to a short-term load prediction technology of a power system, and aims to provide a federated learning load prediction method based on dynamic weighted aggregation. According to the method, an edge computing device adopts local data to carry out neural network training, network parameter change vectors are obtained and uploaded to a cloud server to carry out pairwise similarity calculation to generate a similarity matrix, and a consistency vector between local models in the current round of training is calculated. And then weighting network parameter changes of different local models based on the consistency between the accuracy of each round of local model for the server verification set and the local models to realize a local model cleaning effect, issuing the updated global model to the edge computing device by the cloud server, and repeating the above steps to realize the cleaning effect of the local models. And the server reaches the preset training round number. The problem that a