Open AccessNon-invasive Power Load Monitoring Method Based on Cloud Edge Collaboration
Author(s) -
Gang Yining,
Xuesong Liu,
Tong Donghui,
Zhou Jizan
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/512/1/012115
Subject(s) - cloud computing , computer science , enhanced data rates for gsm evolution , reliability (semiconductor) , renewable energy , power grid , grid , electricity , reliability engineering , smart grid , edge device , power (physics) , distributed computing , real time computing , engineering , telecommunications , electrical engineering , operating system , physics , quantum mechanics , geometry , mathematics
With the large-scale penetration of renewable energy, the safe and stable operation of power grid and economic dispatch are facing great challenges. How to realize accurate perception of internal load characteristics of power users is an important technical difficulty to support power demand side management. For this reason, this article is based on extensive IOT technology in electricity, and a non-invasive power load monitoring method (NILM) based on cloud edge collaboration is proposed. Through the two-level architecture of “edge identification” and “cloud correction”, the method effectively overcomes the contradiction between the weak computing capability of edge terminal and the heavy communication pressure of cloud master station. The experimental results show that the method can effectively improve the accuracy and reliability of traditional NILM method by considering the influence of external factors.