Privacy-Preserving Smart-Meter Analytics for Household Energy Flexibility: A Simulated Demand-Response and Fairness Study

This article presents a simulated empirical study in electrical engineering, data privacy, and energy economics. The synthetic dataset contained 892,800 half-hour household readings and 18,000 household-day observations. A simulated smart-meter dataset represented 600 households with 30-minute electricity demand over one summer month. Households were assigned to six load archetypes based on occupancy, cooling demand, electric cooking, and appliance timing. A day-ahead demand-response program used baseline forecasting and flexible-load shifting. Differential privacy noise was applied to aggregated analytics at three privacy levels. Performance was evaluated through peak reduction, bill savings, forecast error, privacy distortion, and benefit distribution across household archetypes. Moderate privacy protection preserved most simulated demand-response value: system peak fell 11.8% versus 12.6% without privacy noise. Strong privacy protection increased forecast error and reduced savings. Benefit distribution was uneven, with households possessing flexible cooling and appliance loads receiving larger savings than low-consumption households, motivating fairness-aware program design. All numerical values and outcomes are synthetic and are used only to demonstrate a plausible journal-style research design. No real participants, companies, cities, laboratories, utilities, or field sites were studied. The article combines a distinct analytical structure with topic-specific metrics, visuals, and limitations so that the five papers in the issue remain independent in subject and presentation.