Classification of power factor in electric systems by decision trees and neural networks

Reactive power is a component of electrical energy systems to spin machines or convey active energy through power lines. Nevertheless, an overabundance of reactive energy in electric systems can raise the likelihood of transmission system breakdowns. We introduce a method for automated classification of reactive power using a dataset from multifamily residential units, employing chosen techniques from the field of machine learning. Our objective is to forecast excess reactive energy in dwellings in the Northeastern region of the United States by the selected machine-learning approaches, decision trees, and neural networks.