Problem
Research questions and friction points this paper is trying to address.
energy disaggregation
smart meter data
appliance-level consumption
non-intrusive load monitoring
Questionnaire-Guided Disaggregation of Energy Appliance Use for Domestic Smart Meter Data
🤖 AI Summary
This study addresses the challenge of accurately identifying appliance-level energy consumption from 30-minute low-resolution smart meter data. To this end, it proposes an unsupervised load disaggregation system that eliminates the need for per-appliance sub-metering. The approach innovatively integrates data analytics with user questionnaire information, combining a high-power event detection algorithm with questionnaire-guided estimation techniques to decompose aggregate electrical loads into nine distinct appliance categories. Experimental evaluations on four public datasets demonstrate that the proposed system achieves the lowest overall error and significantly outperforms existing baseline methods in monthly disaggregation performance. Furthermore, the framework is capable of delivering personalized energy-saving recommendations to end users.
📝 Abstract
Ireland's smart metering programme records electricity use at 30-minute resolution, with smart meters installed in over 80\% of households as of late 2025. While this is useful for billing of smart, time-of-use tariffs, it is too coarse to capture use of domestic appliances. We present a label-free disaggregation system that breaks usage data into 9 appliance categories by combining event detection for high-power loads with questionnaire-guided estimation. Our evaluation draws on four datasets: a calibration household with a commercial comparator, two public benchmarks (UK-DALE and REFIT) with per-appliance sub-metering, and a smart meter dataset of more than 4,800 years of use from 2,968 Irish consumers. Compared against two independently developed disaggregation systems our hybrid method combining analysis of usage data with questionnaire results, achieves the lowest whole-decomposition error on all buildings across the datasets, with better month-level performance over 54 paired months ($p<0.001$, Holm-corrected). Our method provides useful advice on a household's energy consumption patterns and advice on how to reduce or shift usage on some appliances in order to reduce costs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Label-free disaggregation
Questionnaire-guided estimation
Event detection
Smart meter data
Hybrid method
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