公开(公告)号：US20240103052A1

公开(公告)日：2024-03-28

申请号：US18097234

申请日：2023-01-14

Applicant: ZHEJIANG LAB

Inventor： GANG HUANG ,  WEI HUA ,  ZHOU ZHOU

IPC: G01R21/00 ,  G06N3/042 ,  G06N3/08

CPC classification number: G01R21/001 ,  G06N3/042 ,  G06N3/08

Abstract: The present invention relates to the cross field of smart grid and artificial intelligence, provides a non-intrusive load monitoring method and device based on physics-informed neural network, comprising the following steps: Step 1, obtaining a total load data and an equipment load data of a building in a certain period of time, and using a sliding window method to cut to construct a training data. Step 2, designing a deep learning neural network model to learn the equipment load characteristics contained in the total load data, and outputting the equipment load forecasting. Step 3, based on a physics-constrained learning framework, training the deep learning neural network model by iteratively optimizing the training loss to obtain a trained physics-informed neural network model. Step 4, monitoring the equipment's power consumption in the building according to the output results of the physics-informed neural network model. The present invention can fully extract the operation characteristics of electric equipment, and improve the accuracy of load identification without increasing additional cost.