Abstract
With the development of smart grid, smart meters have been widely used, and the Non-Intrusive Load Monitoring (NILM) technology for sensing and identifying electricity load has gradually matured. Aiming at the problem that the characteristics of industrial users’ load status are few and the gap between different industries is large, this paper uses the random forest algorithm to build a load identification model, and at the same time builds an energy-saving potential evaluation index system including three dimensions of economy, technology and management. Based on the load identification results, users can evaluate the energy-saving potential, and output the use strategies and power consumption suggestions of electric equipment.
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