Feature Analysis of Current Unbalance in Electrical Distribution Systems Using Random Forest
DOI:
https://doi.org/10.65411/rst.2026.266514Keywords:
Current Unbalance, Machine Learning, Random Forest RegressorAbstract
The building sector accounts for more than 130 exajoules (EJ) of global energy consumption, representing approximately 30% of the total energy demand, with a continuous upward trend. Notably, energy demand in buildings surged during the COVID-19 crisis and increased by approximately 20% between 2000 and 2007. A significant portion of this energy consumption is attributed to lighting and air-conditioning systems. The rising electricity demand in buildings adversely impacts power quality, leading to issues such as harmonic distortion, voltage unbalance, and current unbalance in electrical distribution systems. This study investigates the application of the Machine Learning-based Random Forest Regressor model to analyze the causes of current unbalance in a building’s power distribution system. A case study was conducted using electricity consumption data from a facility at the College of Industrial Technology and Management, Rajamangala University of Technology. The analysis results indicate that power features significantly influence current unbalance, with Power Phase A contributing the most at 74.73%, followed by Power Phase C at 10.98% and Power Phase B at 9.55%. These findings provide valuable insights for optimizing maintenance strategies and improving the efficiency of building power distribution systems.
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