Household electricity data granularity impacts socio-demographic inference

Researchers have explored how the temporal granularity of household electricity usage data impacts the ability to infer socio-demographic characteristics. Their study, using a year of data from 1,589 households, found that predictive accuracy remains stable between 15-minute and 1-hour intervals, and again between 1-day and 7-day intervals, suggesting opportunities for data minimization without significant utility loss. The research also indicated that handcrafted features and XGBoost classifiers performed competitively, and that different types of socio-demographic attributes require varying levels of data granularity for accurate inference. AI

IMPACT Provides insights into privacy-utility trade-offs in smart metering data, informing how granular data collection impacts inference capabilities.

RANK_REASON Academic paper detailing a novel analysis of temporal granularity in socio-demographic inference from household load profiles. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

• Dejan Radovanovic
• XGBoost

• paper
• safety

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