Intelligent Household Load Identification Using Multilevel Random Forest on Smart Meters

Israa Al-Mashhadani; Waleed  khaled

doi:10.52866/ijcsm.2024.05.03.019

Authors

Israa Al-Mashhadani Department of Computer Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq
Waleed khaled Department of medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq. https://orcid.org/0000-0003-2528-3943

DOI:

https://doi.org/10.52866/ijcsm.2024.05.03.019

Keywords:

Random Forest Algorithm, Household Load, Smart Meter, Preprocessing Techniques

Abstract

A load identification approach for residential intelligent meters using a random forest (RF)
algorithm is employed 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 by using the random forest method. The power 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, the household 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, improved home power savings rate by
99.2% and the load management efficiency by 98.6%.