Integrated processing technology based on fusion RF algorithm for power grid data assets under background of cloud edge collaboration
CHEN Haomin
LIANG Jinzhao
MA Yun
LI Jinwei
Abstract：Aiming at the problem that most existing methods are difficult to mine the potential value of power grid data,an integrated processing technology based on random forest algorithm and BP neural network for power grid data assets under the background of cloud edge collaboration was proposed.The edge computing nodes close to the grid data source were deployed to build a grid digital asset management system in the cloud edge collaborative environment.At the same time,a classifier was designed by using the random forest algorithm to complete the classification of power grid data types,each type of data was input into the BP neural network for learning,and the corresponding comprehensive processing results were output through continuous iterative optimization.The experimental analysis of as-proposed technology based on Python platform shows that the classification accuracy is more than 90%,adequately effective to improve the processing efficiency of power grid data assets.
Keywords：cloud edge collaborationrandom forest algorithmBP neural networkgrid data assetspower grid digitizationclassifierdata processingload forecasting
Publication Date：2024-01-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 54-59 )