Intelligent Load Identification of Household-Smart Meters Using Multilevel Decision Tree and Data Fusion Techniques

Abstract

The DTA-LI system's fusion data method is crucial in the monitoring of appliance loads for the purposes of improving energy efficiency and management. Common home electrical devices are identified and classified from smart meter data through the analysis of voltage and current variations, allowing for the measurement of energy usage in residential buildings. A load identification system based on a decision tree algorithm may infer information about the residents of a building based on their energy usage habits. Better power savings rates, load shedding management, and overall electrical system performance are the results of the clusters' ability to capture families' purchasing patterns and geo-Demographic segmentation. The DTA-LI system's fusion data method presents a promising avenue for improving residential buildings' energy performance and lowering their carbon footprint, especially in light of the widespread use of smart meters in recent years.