Detecting electricity theft with data

Research focus

My master’s research at AIMS Rwanda explored data analytics for identifying electricity theft. The project reviewed and compared data-based approaches to detection in electricity-consumption records.

Methods

The study considered random forests, decision trees, multilayer perceptrons, and support vector classifiers. It connects an engineering problem—losses in electricity distribution—with statistical learning and model evaluation.