公开(公告)号：US11868869B1

公开(公告)日：2024-01-09

申请号：US18215784

申请日：2023-06-28

Applicant: ZHEJIANG LAB

Inventor： Gang Huang ,  Wei Hua ,  Yongfu Li

IPC: G06N3/04 ,  G06N3/049

CPC classification number: G06N3/049

Abstract: The present invention relates to the field of smart grids, and provides a non-intrusive load monitoring method and device based on temporal attention mechanism. The method comprises the following steps: obtaining a total load data, an equipment load data, and corresponding sampling time of a building during a certain period of time; integrating the total load data and the equipment load data with the corresponding sampling time to obtain an enhanced total load data and an enhanced equipment load data; using a sliding window method to segment the enhanced total load data and the enhanced equipment load data, and constructing a deep learning training dataset; constructing a neural network model based on a deep learning training framework and training the model using the training dataset. The present invention can effectively extract the working time mode of the load and its inherent dependencies, thereby improving the accuracy of load monitoring.

公开(公告)号：US11436494B1

公开(公告)日：2022-09-06

申请号：US17717121

申请日：2022-04-10

Applicant: Zhejiang Lab

Inventor： Gang Huang ,  Longfei Liao ,  Wei Hua

IPC: H02J13/00 ,  H02J4/00 ,  G06N3/08 ,  G06K9/62 ,  G06N3/04

Abstract: An optimal power flow computation method based on multi-task deep learning is provided, which is related to the field of smart power grids. The optimal power flow computation method based on multi-task deep learning includes: acquiring state data of a power grid at a certain dispatching moment, and amplifying collected data samples by means of sampling to acquire training data; applying an optimization method to acquire dispatching solutions of the power grid in different sampling states, and acquiring labels; designing a deep learning neural network model, learning feasibility and an optimal solution of an optimal power flow computation problem separately, and outputting a feasibility determination and an optimal solution prediction; simultaneously training, tasks of the feasibility determination and the optimal solution prediction in the optimal power flow computation problem; and determining whether there is a feasible dispatching solution, and outputting an optimal dispatching solution or an early warning.