aiqbench analysis
This paper presents a novel label-free disaggregation system that leverages questionnaire-guided estimation alongside event detection to identify nine appliance categories from coarse smart meter data. The hybrid approach significantly outperforms existing independent systems in decomposing household energy usage, offering actionable insights for optimizing consumption patterns and reducing costs. By integrating user feedback directly into the analysis pipeline, this method advances the practical application of AI in domestic energy management without requiring invasive sub-metering hardware.
Questionnaire-Guided Disaggregation of Energy Appliance Use for Domestic Smart Meter Data
arXiv:2610.01297v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2610.01297 [cs.AI]
(or arXiv:2610.01297v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.01297 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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