公开(公告)号：US20240256829A1

公开(公告)日：2024-08-01

申请号：US18243107

申请日：2023-09-07

申请人： University of Science and Technology Beijing

发明人： Tianyu HU ,  Huimin MA ,  Xiao ZHANG ,  Hao LIU ,  Kangsheng WANG

IPC分类号： G06N3/042 ,  G06N3/084

CPC分类号： G06N3/042 ,  G06N3/084

摘要： A wind power generation quantile prediction method based on machine mental model and self-attention includes: using human cognitive decision-making mechanism for reference to construct the machine mental model as the basic framework of WQPMMSA, and then the seasonal power generation rules and intraday power generation trend are encoded into WQPMMSA as the input information of the prediction method, using the self-attention layer to replace the recurrent neural network in the original machine mental model, and establishing the statistical relationship between the seasonal power generation rules and the intraday power generation trend effectively, reducing the long-range forgetting of the original machine mental model-convert the continuous rank probability score in the integral form into a summation form, and using it as a loss function to train WQPMMSA, so that WQPMMSA approaches the optimal quantile prediction result with the highest efficiency. Therefore, accurate quantile prediction of wind power generation is realized.