"Electricity purchase history "

"Electricity theft represents a significant source of non-technical losses (NTLs) in power distribution networks, leading to substantial financial deficits, grid instability, and safety concerns. This study introduces a machine learning-based methodology to detect and predict electricity theft by examining residential consumption patterns in Pietermaritzburg Municipality, South Africa. Utilising meter data from approximately 42,000 customers, of whom 21,935 made at least one electricity purchase over a five-year period (2019\u20132024), the research evaluates five machine learning techniques: decision trees, logistic regression, K-nearest neighbours, XGBoost, and random forests. The approach integrates survey data, audit outcomes, and audit evaluations for a holistic analysis. Results indicate that Random Forest outperforms other models, achieving the highest scores across accuracy (0.8), precision (0.75), recall (0.9), and F1-score (0.82) for detecting tampering. Survey findings reveal that 85.0% of respondents link theft to financial hardship, underscoring socio-economic influences. This study provides utilities with practical insights to reduce NTLs, enhance grid reliability, and foster sustainable energy management."