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Abstract

A load identification approach for residential intelligent meters using a random forest (RF) algorithm isemployed to guarantee the secure and cost-effective functioning of the electricity grid. In this study, the load data from a smart meter in a home was pre-processed to remove any gaps, noise, or inconsistencies before making any predictions byusing the random forest method. Thepower quality (PQ)features, current features, and Voltage-Current (V-I features), as well as the forecast findings and mathematical tools were used to recognise the load. Using these tools, thehousehold intelligent meters utilising the random forest algorithm, features, harmonic characteristics, and instantaneous characteristics were extracted to form the load characteristics, and the objective function of load identification was generated based on a set of features. The findings of this comparative study demonstrate that employing this technique can reduce identification errors and boost productivity by a full two seconds. The proposed approach, based on a random forest technique, improvedhome power savings rate by 99.2% and the load management efficiency by 98.6%

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